This is octave.info, produced by makeinfo version 5.2 from octave.texi.

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* Octave: (octave).           Interactive language for numerical computations.

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   Copyright (C) 1996-2013 John W. Eaton.

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File: octave.info,  Node: Managing Default Properties,  Prev: Searching Properties,  Up: Graphics Data Structures

15.3.5 Managing Default Properties
----------------------------------

Object properties have two classes of default values, "factory defaults"
(the initial values) and "user-defined defaults", which may override the
factory defaults.

   Although default values may be set for any object, they are set in
parent objects and apply to child objects, of the specified object type.
For example, setting the default 'color' property of 'line' objects to
"green", for the 'root' object, will result in all 'line' objects
inheriting the 'color' "green" as the default value.

     set (0, "defaultlinecolor", "green");

sets the default line color for all objects.  The rule for constructing
the property name to set a default value is

     default + OBJECT-TYPE + PROPERTY-NAME

   This rule can lead to some strange looking names, for example
'defaultlinelinewidth"' specifies the default 'linewidth' property for
'line' objects.

   The example above used the root figure object, 0, so the default
property value will apply to all line objects.  However, default values
are hierarchical, so defaults set in a figure objects override those set
in the root figure object.  Likewise, defaults set in axes objects
override those set in figure or root figure objects.  For example,

     subplot (2, 1, 1);
     set (0, "defaultlinecolor", "red");
     set (1, "defaultlinecolor", "green");
     set (gca (), "defaultlinecolor", "blue");
     line (1:10, rand (1, 10));
     subplot (2, 1, 2);
     line (1:10, rand (1, 10));
     figure (2)
     line (1:10, rand (1, 10));

produces two figures.  The line in first subplot window of the first
figure is blue because it inherits its color from its parent axes
object.  The line in the second subplot window of the first figure is
green because it inherits its color from its parent figure object.  The
line in the second figure window is red because it inherits its color
from the global root figure parent object.

   To remove a user-defined default setting, set the default property to
the value "remove".  For example,

     set (gca (), "defaultlinecolor", "remove");

removes the user-defined default line color setting from the current
axes object.  To quickly remove all user-defined defaults use the
'reset' function.

 -- Built-in Function: reset (H)
     Reset the properties of the graphic object H to their default
     values.

     For figures, the properties "position", "units", "windowstyle", and
     "paperunits" are not affected.  For axes, the properties "position"
     and "units" are not affected.

     The input H may also be a vector of graphic handles in which case
     each individual object will be reset.

     See also: *note cla: XREFcla, *note clf: XREFclf, *note newplot:
     XREFnewplot.

   Getting the "default" property of an object returns a list of
user-defined defaults set for the object.  For example,

     get (gca (), "default");

returns a list of user-defined default values for the current axes
object.

   Factory default values are stored in the root figure object.  The
command

     get (0, "factory");

returns a list of factory defaults.


File: octave.info,  Node: Advanced Plotting,  Prev: Graphics Data Structures,  Up: Plotting

15.4 Advanced Plotting
======================

* Menu:

* Colors::
* Line Styles::
* Marker Styles::
* Callbacks::
* Application-defined Data::
* Object Groups::
* Graphics Toolkits::


File: octave.info,  Node: Colors,  Next: Line Styles,  Up: Advanced Plotting

15.4.1 Colors
-------------

Colors may be specified as RGB triplets with values ranging from zero to
one, or by name.  Recognized color names include "blue", "black",
"cyan", "green", "magenta", "red", "white", and "yellow".


File: octave.info,  Node: Line Styles,  Next: Marker Styles,  Prev: Colors,  Up: Advanced Plotting

15.4.2 Line Styles
------------------

Line styles are specified by the following properties:

'linestyle'
     May be one of

     "-"
          Solid line.  [default]

     "--"
          Dashed line.

     ":"
          Dotted line.

     "-."
          A dash-dot line.

     "none"
          No line.  Points will still be marked using the current Marker
          Style.

'linewidth'
     A number specifying the width of the line.  The default is 1.  A
     value of 2 is twice as wide as the default, etc.


File: octave.info,  Node: Marker Styles,  Next: Callbacks,  Prev: Line Styles,  Up: Advanced Plotting

15.4.3 Marker Styles
--------------------

Marker styles are specified by the following properties:

'marker'
     A character indicating a plot marker to be place at each data
     point, or "none", meaning no markers should be displayed.

'markeredgecolor'
     The color of the edge around the marker, or "auto", meaning that
     the edge color is the same as the face color.  *Note Colors::.

'markerfacecolor'
     The color of the marker, or "none" to indicate that the marker
     should not be filled.  *Note Colors::.

'markersize'
     A number specifying the size of the marker.  The default is 1.  A
     value of 2 is twice as large as the default, etc.

   The 'colstyle' function will parse a 'plot'-style specification and
will return the color, line, and marker values that would result.

 -- Function File: [STYLE, COLOR, MARKER, MSG] = colstyle (LINESPEC)
     Parse LINESPEC and return the line style, color, and markers given.
     In the case of an error, the string MSG will return the text of the
     error.


File: octave.info,  Node: Callbacks,  Next: Application-defined Data,  Prev: Marker Styles,  Up: Advanced Plotting

15.4.4 Callbacks
----------------

Callback functions can be associated with graphics objects and triggered
after certain events occur.  The basic structure of all callback
function is

     function mycallback (src, data)
     ...
     endfunction

where 'src' gives a handle to the source of the callback, and 'code'
gives some event specific data.  This can then be associated with an
object either at the objects creation or later with the 'set' function.
For example,

     plot (x, "DeleteFcn", @(s, e) disp ("Window Deleted"))

where at the moment that the plot is deleted, the message "Window
Deleted" will be displayed.

   Additional user arguments can be passed to callback functions, and
will be passed after the 2 default arguments.  For example:

     plot (x, "DeleteFcn", {@mycallback, "1"})
     ...
     function mycallback (src, data, a1)
       fprintf ("Closing plot %d\n", a1);
     endfunction

   The basic callback functions that are available for all graphics
objects are

   * CreateFcn This is the callback that is called at the moment of the
     objects creation.  It is not called if the object is altered in any
     way, and so it only makes sense to define this callback in the
     function call that defines the object.  Callbacks that are added to
     'CreateFcn' later with the 'set' function will never be executed.

   * DeleteFcn This is the callback that is called at the moment an
     object is deleted.

   * ButtonDownFcn This is the callback that is called if a mouse button
     is pressed while the pointer is over this object.  Note, that the
     gnuplot interface does not respect this callback.

   The object and figure that the event occurred in that resulted in the
callback being called can be found with the 'gcbo' and 'gcbf' functions.

 -- Function File: H = gcbo ()
 -- Function File: [H, FIG] = gcbo ()
     Return a handle to the object whose callback is currently
     executing.

     If no callback is executing, this function returns the empty
     matrix.  This handle is obtained from the root object property
     "CallbackObject".

     When called with a second output argument, return the handle of the
     figure containing the object whose callback is currently executing.
     If no callback is executing the second output is also set to the
     empty matrix.

     See also: *note gcbf: XREFgcbf, *note gco: XREFgco, *note gca:
     XREFgca, *note gcf: XREFgcf, *note get: XREFget, *note set:
     XREFset.

 -- Function File: FIG = gcbf ()
     Return a handle to the figure containing the object whose callback
     is currently executing.

     If no callback is executing, this function returns the empty
     matrix.  The handle returned by this function is the same as the
     second output argument of 'gcbo'.

     See also: *note gcbo: XREFgcbo, *note gcf: XREFgcf, *note gco:
     XREFgco, *note gca: XREFgca, *note get: XREFget, *note set:
     XREFset.

   Callbacks can equally be added to properties with the 'addlistener'
function described below.


File: octave.info,  Node: Application-defined Data,  Next: Object Groups,  Prev: Callbacks,  Up: Advanced Plotting

15.4.5 Application-defined Data
-------------------------------

Octave has a provision for attaching application-defined data to a
graphics handle.  The data can be anything which is meaningful to the
application, and will be completely ignored by Octave.

 -- Function File: setappdata (H, NAME, VALUE)
 -- Function File: setappdata (H, NAME1, VALUE1, NAME2, VALUE3, ...)
     Set the application data NAME to VALUE for the graphics object with
     handle H.

     H may also be a vector of graphics handles.  If the application
     data with the specified NAME does not exist, it is created.
     Multiple NAME/VALUE pairs can be specified at a time.

     See also: *note getappdata: XREFgetappdata, *note isappdata:
     XREFisappdata, *note rmappdata: XREFrmappdata, *note guidata:
     XREFguidata, *note get: XREFget, *note set: XREFset, *note getpref:
     XREFgetpref, *note setpref: XREFsetpref.

 -- Function File: VALUE = getappdata (H, NAME)
 -- Function File: APPDATA = getappdata (H)
     Return the VALUE of the application data NAME for the graphics
     object with handle H.

     H may also be a vector of graphics handles.  If no second argument
     NAME is given then 'getappdata' returns a structure, APPDATA, whose
     fields correspond to the appdata properties.

     See also: *note setappdata: XREFsetappdata, *note isappdata:
     XREFisappdata, *note rmappdata: XREFrmappdata, *note guidata:
     XREFguidata, *note get: XREFget, *note set: XREFset, *note getpref:
     XREFgetpref, *note setpref: XREFsetpref.

 -- Function File: rmappdata (H, NAME)
 -- Function File: rmappdata (H, NAME1, NAME2, ...)
     Delete the application data NAME from the graphics object with
     handle H.

     H may also be a vector of graphics handles.  Multiple application
     data names may be supplied to delete several properties at once.

     See also: *note setappdata: XREFsetappdata, *note getappdata:
     XREFgetappdata, *note isappdata: XREFisappdata.

 -- Function File: VALID = isappdata (H, NAME)
     Return true if the named application data, NAME, exists for the
     graphics object with handle H.

     H may also be a vector of graphics handles.

     See also: *note getappdata: XREFgetappdata, *note setappdata:
     XREFsetappdata, *note rmappdata: XREFrmappdata, *note guidata:
     XREFguidata, *note get: XREFget, *note set: XREFset, *note getpref:
     XREFgetpref, *note setpref: XREFsetpref.


File: octave.info,  Node: Object Groups,  Next: Graphics Toolkits,  Prev: Application-defined Data,  Up: Advanced Plotting

15.4.6 Object Groups
--------------------

A number of Octave high level plot functions return groups of other
graphics objects or they return graphics objects that have their
properties linked in such a way that changes to one of the properties
results in changes in the others.  A graphic object that groups other
objects is an 'hggroup'

 -- Function File: hggroup ()
 -- Function File: hggroup (HAX)
 -- Function File: hggroup (..., PROPERTY, VALUE, ...)
 -- Function File: H = hggroup (...)
     Create handle graphics group object with axes parent HAX.

     If no parent is specified, the group is created in the current
     axes.

     Multiple property/value pairs may be specified for the hggroup, but
     they must appear in pairs.

     The optional return value H is a graphics handle to the created
     hggroup object.

     Programming Note: An hggroup is a way to group base graphics
     objects such as line objects or patch objects into a single unit
     which can react appropriately.  For example, the individual lines
     of a contour plot are collected into a single hggroup so that they
     can be made visible/invisible with a single command, 'set
     (hg_handle, "visible", "off")'.

     See also: *note addproperty: XREFaddproperty, *note addlistener:
     XREFaddlistener.

   For example a simple use of a 'hggroup' might be

     x = 0:0.1:10;
     hg = hggroup ();
     plot (x, sin (x), "color", [1, 0, 0], "parent", hg);
     hold on
     plot (x, cos (x), "color", [0, 1, 0], "parent", hg);
     set (hg, "visible", "off");

which groups the two plots into a single object and controls their
visibility directly.  The default properties of an 'hggroup' are the
same as the set of common properties for the other graphics objects.
Additional properties can be added with the 'addproperty' function.

 -- Built-in Function: addproperty (NAME, H, TYPE)
 -- Built-in Function: addproperty (NAME, H, TYPE, ARG, ...)
     Create a new property named NAME in graphics object H.  TYPE
     determines the type of the property to create.  ARGS usually
     contains the default value of the property, but additional
     arguments might be given, depending on the type of the property.

     The supported property types are:

     'string'
          A string property.  ARG contains the default string value.

     'any'
          An un-typed property.  This kind of property can hold any
          octave value.  ARGS contains the default value.

     'radio'
          A string property with a limited set of accepted values.  The
          first argument must be a string with all accepted values
          separated by a vertical bar ('|').  The default value can be
          marked by enclosing it with a '{' '}' pair.  The default value
          may also be given as an optional second string argument.

     'boolean'
          A boolean property.  This property type is equivalent to a
          radio property with "on|off" as accepted values.  ARG contains
          the default property value.

     'double'
          A scalar double property.  ARG contains the default value.

     'handle'
          A handle property.  This kind of property holds the handle of
          a graphics object.  ARG contains the default handle value.
          When no default value is given, the property is initialized to
          the empty matrix.

     'data'
          A data (matrix) property.  ARG contains the default data
          value.  When no default value is given, the data is
          initialized to the empty matrix.

     'color'
          A color property.  ARG contains the default color value.  When
          no default color is given, the property is set to black.  An
          optional second string argument may be given to specify an
          additional set of accepted string values (like a radio
          property).

     TYPE may also be the concatenation of a core object type and a
     valid property name for that object type.  The property created
     then has the same characteristics as the referenced property (type,
     possible values, hidden state...).  This allows to clone an
     existing property into the graphics object H.

     Examples:

          addproperty ("my_property", gcf, "string", "a string value");
          addproperty ("my_radio", gcf, "radio", "val_1|val_2|{val_3}");
          addproperty ("my_style", gcf, "linelinestyle", "--");

     See also: *note addlistener: XREFaddlistener, *note hggroup:
     XREFhggroup.

   Once a property in added to an 'hggroup', it is not linked to any
other property of either the children of the group, or any other
graphics object.  Add so to control the way in which this newly added
property is used, the 'addlistener' function is used to define a
callback function that is executed when the property is altered.

 -- Built-in Function: addlistener (H, PROP, FCN)
     Register FCN as listener for the property PROP of the graphics
     object H.  Property listeners are executed (in order of
     registration) when the property is set.  The new value is already
     available when the listeners are executed.

     PROP must be a string naming a valid property in H.

     FCN can be a function handle, a string or a cell array whose first
     element is a function handle.  If FCN is a function handle, the
     corresponding function should accept at least 2 arguments, that
     will be set to the object handle and the empty matrix respectively.
     If FCN is a string, it must be any valid octave expression.  If FCN
     is a cell array, the first element must be a function handle with
     the same signature as described above.  The next elements of the
     cell array are passed as additional arguments to the function.

     Example:

          function my_listener (h, dummy, p1)
            fprintf ("my_listener called with p1=%s\n", p1);
          endfunction

          addlistener (gcf, "position", {@my_listener, "my string"})

     See also: *note addproperty: XREFaddproperty, *note hggroup:
     XREFhggroup.

 -- Built-in Function: dellistener (H, PROP, FCN)
     Remove the registration of FCN as a listener for the property PROP
     of the graphics object H.  The function FCN must be the same
     variable (not just the same value), as was passed to the original
     call to 'addlistener'.

     If FCN is not defined then all listener functions of PROP are
     removed.

     Example:

          function my_listener (h, dummy, p1)
            fprintf ("my_listener called with p1=%s\n", p1);
          endfunction

          c = {@my_listener, "my string"};
          addlistener (gcf, "position", c);
          dellistener (gcf, "position", c);

   An example of the use of these two functions might be

     x = 0:0.1:10;
     hg = hggroup ();
     h = plot (x, sin (x), "color", [1, 0, 0], "parent", hg);
     addproperty ("linestyle", hg, "linelinestyle", get (h, "linestyle"));
     addlistener (hg, "linestyle", @update_props);
     hold on
     plot (x, cos (x), "color", [0, 1, 0], "parent", hg);

     function update_props (h, d)
       set (get (h, "children"), "linestyle", get (h, "linestyle"));
     endfunction

that adds a 'linestyle' property to the 'hggroup' and propagating any
changes its value to the children of the group.  The 'linkprop' function
can be used to simplify the above to be

     x = 0:0.1:10;
     hg = hggroup ();
     h1 = plot (x, sin (x), "color", [1, 0, 0], "parent", hg);
     addproperty ("linestyle", hg, "linelinestyle", get (h, "linestyle"));
     hold on
     h2 = plot (x, cos (x), "color", [0, 1, 0], "parent", hg);
     hlink = linkprop ([hg, h1, h2], "color");

 -- Function File: HLINK = linkprop (H, "PROP")
 -- Function File: HLINK = linkprop (H, {"PROP1", "PROP2", ...})
     Link graphic object properties, such that a change in one is
     propagated to the others.

     The input H is a vector of graphic handles to link.

     PROP may be a string when linking a single property, or a cell
     array of strings for multiple properties.  During the linking
     process all properties in PROP will initially be set to the values
     that exist on the first object in the list H.

     The function returns HLINK which is a special object describing the
     link.  As long as the reference HLINK exists the link between
     graphic objects will be active.  This means that HLINK must be
     preserved in a workspace variable, a global variable, or otherwise
     stored using a function such as 'setappdata', 'guidata'.  To unlink
     properties, execute 'clear HLINK'.

     An example of the use of 'linkprop' is

          x = 0:0.1:10;
          subplot (1,2,1);
          h1 = plot (x, sin (x));
          subplot (1,2,2);
          h2 = plot (x, cos (x));
          hlink = linkprop ([h1, h2], {"color","linestyle"});
          set (h1, "color", "green");
          set (h2, "linestyle", "--");

     See also: *note linkaxes: XREFlinkaxes.

 -- Function File: linkaxes (HAX)
 -- Function File: linkaxes (HAX, OPTSTR)
     Link the axis limits of 2-D plots such that a change in one is
     propagated to the others.

     The axes handles to be linked are passed as the first argument HAX.

     The optional second argument is a string which defines which axis
     limits will be linked.  The possible values for OPTSTR are:

     "x"
          Link x-axes

     "y"
          Link y-axes

     "xy" (default)
          Link both axes

     "off"
          Turn off linking

     If unspecified the default is to link both X and Y axes.

     When linking, the limits from the first axes in HAX are applied to
     the other axes in the list.  Subsequent changes to any one of the
     axes will be propagated to the others.

     See also: *note linkprop: XREFlinkprop, *note addproperty:
     XREFaddproperty.

   These capabilities are used in a number of basic graphics objects.
The 'hggroup' objects created by the functions of Octave contain one or
more graphics object and are used to:

   * group together multiple graphics objects,

   * create linked properties between different graphics objects, and

   * to hide the nominal user data, from the actual data of the objects.

For example the 'stem' function creates a stem series where each
'hggroup' of the stem series contains two line objects representing the
body and head of the stem.  The 'ydata' property of the 'hggroup' of the
stem series represents the head of the stem, whereas the body of the
stem is between the baseline and this value.  For example

     h = stem (1:4)
     get (h, "xdata")
     => [  1   2   3   4]'
     get (get (h, "children")(1), "xdata")
     => [  1   1 NaN   2   2 NaN   3   3 NaN   4   4 NaN]'

shows the difference between the 'xdata' of the 'hggroup' of a stem
series object and the underlying line.

   The basic properties of such group objects is that they consist of
one or more linked 'hggroup', and that changes in certain properties of
these groups are propagated to other members of the group.  Whereas,
certain properties of the members of the group only apply to the current
member.

   In addition the members of the group can also be linked to other
graphics objects through callback functions.  For example the baseline
of the 'bar' or 'stem' functions is a line object, whose length and
position are automatically adjusted, based on changes to the
corresponding hggroup elements.

* Menu:

* Data Sources in Object Groups::
* Area Series::
* Bar Series::
* Contour Groups::
* Error Bar Series::
* Line Series::
* Quiver Group::
* Scatter Group::
* Stair Group::
* Stem Series::
* Surface Group::


File: octave.info,  Node: Data Sources in Object Groups,  Next: Area Series,  Up: Object Groups

15.4.6.1 Data Sources in Object Groups
......................................

All of the group objects contain data source parameters.  There are
string parameters that contain an expression that is evaluated to update
the relevant data property of the group when the 'refreshdata' function
is called.

 -- Function File: refreshdata ()
 -- Function File: refreshdata (H)
 -- Function File: refreshdata (H, WORKSPACE)
     Evaluate any 'datasource' properties of the current figure and
     update the plot if the corresponding data has changed.

     If the first argument H is a list of graphic handles, then operate
     on these objects rather than the current figure returned by 'gcf'.

     The optional second argument WORKSPACE can take the following
     values:

     "base"
          Evaluate the datasource properties in the base workspace.
          (default).

     "caller"
          Evaluate the datasource properties in the workspace of the
          function that called 'refreshdata'.

     An example of the use of 'refreshdata' is:

          x = 0:0.1:10;
          y = sin (x);
          plot (x, y, "ydatasource", "y");
          for i = 1 : 100
            pause (0.1);
            y = sin (x + 0.1*i);
            refreshdata ();
          endfor


File: octave.info,  Node: Area Series,  Next: Bar Series,  Prev: Data Sources in Object Groups,  Up: Object Groups

15.4.6.2 Area Series
....................

Area series objects are created by the 'area' function.  Each of the
'hggroup' elements contains a single patch object.  The properties of
the area series are

'basevalue'
     The value where the base of the area plot is drawn.

'linewidth'
'linestyle'
     The line width and style of the edge of the patch objects making up
     the areas.  *Note Line Styles::.

'edgecolor'
'facecolor'
     The line and fill color of the patch objects making up the areas.
     *Note Colors::.

'xdata'
'ydata'
     The x and y coordinates of the original columns of the data passed
     to 'area' prior to the cumulative summation used in the 'area'
     function.

'xdatasource'
'ydatasource'
     Data source variables.


File: octave.info,  Node: Bar Series,  Next: Contour Groups,  Prev: Area Series,  Up: Object Groups

15.4.6.3 Bar Series
...................

Bar series objects are created by the 'bar' or 'barh' functions.  Each
'hggroup' element contains a single patch object.  The properties of the
bar series are

'showbaseline'
'baseline'
'basevalue'
     The property 'showbaseline' flags whether the baseline of the bar
     series is displayed (default is "on").  The handle of the graphics
     object representing the baseline is given by the 'baseline'
     property and the y-value of the baseline by the 'basevalue'
     property.

     Changes to any of these properties are propagated to the other
     members of the bar series and to the baseline itself.  Equally,
     changes in the properties of the base line itself are propagated to
     the members of the corresponding bar series.

'barwidth'
'barlayout'
'horizontal'
     The property 'barwidth' is the width of the bar corresponding to
     the WIDTH variable passed to 'bar' or BARH.  Whether the bar series
     is "grouped" or "stacked" is determined by the 'barlayout' property
     and whether the bars are horizontal or vertical by the 'horizontal'
     property.

     Changes to any of these property are propagated to the other
     members of the bar series.

'linewidth'
'linestyle'
     The line width and style of the edge of the patch objects making up
     the bars.  *Note Line Styles::.

'edgecolor'
'facecolor'
     The line and fill color of the patch objects making up the bars.
     *Note Colors::.

'xdata'
     The nominal x positions of the bars.  Changes in this property and
     propagated to the other members of the bar series.

'ydata'
     The y value of the bars in the 'hggroup'.

'xdatasource'
'ydatasource'
     Data source variables.


File: octave.info,  Node: Contour Groups,  Next: Error Bar Series,  Prev: Bar Series,  Up: Object Groups

15.4.6.4 Contour Groups
.......................

Contour group objects are created by the 'contour', 'contourf' and
'contour3' functions.  The are equally one of the handles returned by
the 'surfc' and 'meshc' functions.  The properties of the contour group
are

'contourmatrix'
     A read only property that contains the data return by 'contourc'
     used to create the contours of the plot.

'fill'
     A radio property that can have the values "on" or "off" that flags
     whether the contours to plot are to be filled.

'zlevelmode'
'zlevel'
     The radio property 'zlevelmode' can have the values "none", "auto",
     or "manual".  When its value is "none" there is no z component to
     the plotted contours.  When its value is "auto" the z value of the
     plotted contours is at the same value as the contour itself.  If
     the value is "manual", then the z value at which to plot the
     contour is determined by the 'zlevel' property.

'levellistmode'
'levellist'
'levelstepmode'
'levelstep'
     If 'levellistmode' is "manual", then the levels at which to plot
     the contours is determined by 'levellist'.  If 'levellistmode' is
     set to "auto", then the distance between contours is determined by
     'levelstep'.  If both 'levellistmode' and 'levelstepmode' are set
     to "auto", then there are assumed to be 10 equal spaced contours.

'textlistmode'
'textlist'
'textstepmode'
'textstep'
     If 'textlistmode' is "manual", then the labeled contours is
     determined by 'textlist'.  If 'textlistmode' is set to "auto", then
     the distance between labeled contours is determined by 'textstep'.
     If both 'textlistmode' and 'textstepmode' are set to "auto", then
     there are assumed to be 10 equal spaced labeled contours.

'showtext'
     Flag whether the contour labels are shown or not.

'labelspacing'
     The distance between labels on a single contour in points.

'linewidth'

'linestyle'

'linecolor'
     The properties of the contour lines.  The properties 'linewidth'
     and 'linestyle' are similar to the corresponding properties for
     lines.  The property 'linecolor' is a color property (*note
     Colors::), that can also have the values of "none" or "auto".  If
     'linecolor' is "none", then no contour line is drawn.  If
     'linecolor' is "auto" then the line color is determined by the
     colormap.

'xdata'
'ydata'
'zdata'
     The original x, y, and z data of the contour lines.

'xdatasource'
'ydatasource'
'zdatasource'
     Data source variables.


File: octave.info,  Node: Error Bar Series,  Next: Line Series,  Prev: Contour Groups,  Up: Object Groups

15.4.6.5 Error Bar Series
.........................

Error bar series are created by the 'errorbar' function.  Each 'hggroup'
element contains two line objects representing the data and the
errorbars separately.  The properties of the error bar series are

'color'
     The RGB color or color name of the line objects of the error bars.
     *Note Colors::.

'linewidth'
'linestyle'
     The line width and style of the line objects of the error bars.
     *Note Line Styles::.

'marker'
'markeredgecolor'
'markerfacecolor'
'markersize'
     The line and fill color of the markers on the error bars.  *Note
     Colors::.

'xdata'
'ydata'
'ldata'
'udata'
'xldata'
'xudata'
     The original x, y, l, u, xl, xu data of the error bars.

'xdatasource'
'ydatasource'
'ldatasource'
'udatasource'
'xldatasource'
'xudatasource'
     Data source variables.


File: octave.info,  Node: Line Series,  Next: Quiver Group,  Prev: Error Bar Series,  Up: Object Groups

15.4.6.6 Line Series
....................

Line series objects are created by the 'plot' and 'plot3' functions and
are of the type 'line'.  The properties of the line series with the
ability to add data sources.

'color'
     The RGB color or color name of the line objects.  *Note Colors::.

'linewidth'
'linestyle'
     The line width and style of the line objects.  *Note Line Styles::.

'marker'
'markeredgecolor'
'markerfacecolor'
'markersize'
     The line and fill color of the markers.  *Note Colors::.

'xdata'
'ydata'
'zdata'
     The original x, y and z data.

'xdatasource'
'ydatasource'
'zdatasource'
     Data source variables.


File: octave.info,  Node: Quiver Group,  Next: Scatter Group,  Prev: Line Series,  Up: Object Groups

15.4.6.7 Quiver Group
.....................

Quiver series objects are created by the 'quiver' or 'quiver3'
functions.  Each 'hggroup' element of the series contains three line
objects as children representing the body and head of the arrow,
together with a marker as the point of origin of the arrows.  The
properties of the quiver series are

'autoscale'
'autoscalefactor'
     Flag whether the length of the arrows is scaled or defined directly
     from the U, V and W data.  If the arrow length is flagged as being
     scaled by the 'autoscale' property, then the length of the
     autoscaled arrow is controlled by the 'autoscalefactor'.

'maxheadsize'
     This property controls the size of the head of the arrows in the
     quiver series.  The default value is 0.2.

'showarrowhead'
     Flag whether the arrow heads are displayed in the quiver plot.

'color'
     The RGB color or color name of the line objects of the quiver.
     *Note Colors::.

'linewidth'
'linestyle'
     The line width and style of the line objects of the quiver.  *Note
     Line Styles::.

'marker'
'markerfacecolor'
'markersize'
     The line and fill color of the marker objects at the original of
     the arrows.  *Note Colors::.

'xdata'
'ydata'
'zdata'
     The origins of the values of the vector field.

'udata'
'vdata'
'wdata'
     The values of the vector field to plot.

'xdatasource'
'ydatasource'
'zdatasource'
'udatasource'
'vdatasource'
'wdatasource'
     Data source variables.


File: octave.info,  Node: Scatter Group,  Next: Stair Group,  Prev: Quiver Group,  Up: Object Groups

15.4.6.8 Scatter Group
......................

Scatter series objects are created by the 'scatter' or 'scatter3'
functions.  A single hggroup element contains as many children as there
are points in the scatter plot, with each child representing one of the
points.  The properties of the stem series are

'linewidth'
     The line width of the line objects of the points.  *Note Line
     Styles::.

'marker'
'markeredgecolor'
'markerfacecolor'
     The line and fill color of the markers of the points.  *Note
     Colors::.

'xdata'
'ydata'
'zdata'
     The original x, y and z data of the stems.

'cdata'
     The color data for the points of the plot.  Each point can have a
     separate color, or a unique color can be specified.

'sizedata'
     The size data for the points of the plot.  Each point can its own
     size or a unique size can be specified.

'xdatasource'
'ydatasource'
'zdatasource'
'cdatasource'
'sizedatasource'
     Data source variables.


File: octave.info,  Node: Stair Group,  Next: Stem Series,  Prev: Scatter Group,  Up: Object Groups

15.4.6.9 Stair Group
....................

Stair series objects are created by the 'stair' function.  Each
'hggroup' element of the series contains a single line object as a child
representing the stair.  The properties of the stair series are

'color'
     The RGB color or color name of the line objects of the stairs.
     *Note Colors::.

'linewidth'
'linestyle'
     The line width and style of the line objects of the stairs.  *Note
     Line Styles::.

'marker'
'markeredgecolor'
'markerfacecolor'
'markersize'
     The line and fill color of the markers on the stairs.  *Note
     Colors::.

'xdata'
'ydata'
     The original x and y data of the stairs.

'xdatasource'
'ydatasource'
     Data source variables.


File: octave.info,  Node: Stem Series,  Next: Surface Group,  Prev: Stair Group,  Up: Object Groups

15.4.6.10 Stem Series
.....................

Stem series objects are created by the 'stem' or 'stem3' functions.
Each 'hggroup' element contains a single line object as a child
representing the stems.  The properties of the stem series are

'showbaseline'
'baseline'
'basevalue'
     The property 'showbaseline' flags whether the baseline of the stem
     series is displayed (default is "on").  The handle of the graphics
     object representing the baseline is given by the 'baseline'
     property and the y-value (or z-value for 'stem3') of the baseline
     by the 'basevalue' property.

     Changes to any of these property are propagated to the other
     members of the stem series and to the baseline itself.  Equally
     changes in the properties of the base line itself are propagated to
     the members of the corresponding stem series.

'color'
     The RGB color or color name of the line objects of the stems.
     *Note Colors::.

'linewidth'
'linestyle'
     The line width and style of the line objects of the stems.  *Note
     Line Styles::.

'marker'
'markeredgecolor'
'markerfacecolor'
'markersize'
     The line and fill color of the markers on the stems.  *Note
     Colors::.

'xdata'
'ydata'
'zdata'
     The original x, y and z data of the stems.

'xdatasource'
'ydatasource'
'zdatasource'
     Data source variables.


File: octave.info,  Node: Surface Group,  Prev: Stem Series,  Up: Object Groups

15.4.6.11 Surface Group
.......................

Surface group objects are created by the 'surf' or 'mesh' functions, but
are equally one of the handles returned by the 'surfc' or 'meshc'
functions.  The surface group is of the type 'surface'.

   The properties of the surface group are

'edgecolor'

'facecolor'
     The RGB color or color name of the edges or faces of the surface.
     *Note Colors::.

'linewidth'
'linestyle'
     The line width and style of the lines on the surface.  *Note Line
     Styles::.

'marker'
'markeredgecolor'
'markerfacecolor'
'markersize'
     The line and fill color of the markers on the surface.  *Note
     Colors::.

'xdata'
'ydata'
'zdata'
'cdata'
     The original x, y, z and c data.

'xdatasource'
'ydatasource'
'zdatasource'
'cdatasource'
     Data source variables.


File: octave.info,  Node: Graphics Toolkits,  Prev: Object Groups,  Up: Advanced Plotting

15.4.7 Graphics Toolkits
------------------------

 -- Function File: NAME = graphics_toolkit ()
 -- Function File: NAME = graphics_toolkit (HLIST)
 -- Function File: graphics_toolkit (NAME)
 -- Function File: graphics_toolkit (HLIST, NAME)
     Query or set the default graphics toolkit which is assigned to new
     figures.

     With no inputs, return the current default graphics toolkit.  If
     the input is a list of figure graphic handles, HLIST, then return
     the name of the graphics toolkit in use for each figure.

     When called with a single input NAME set the default graphics
     toolkit to NAME.  If the toolkit is not already loaded, it is
     initialized by calling the function '__init_NAME__'.  If the first
     input is a list of figure handles, HLIST, then the graphics toolkit
     is set to NAME for these figures only.

     See also: *note available_graphics_toolkits:
     XREFavailable_graphics_toolkits.

 -- Built-in Function: available_graphics_toolkits ()
     Return a cell array of registered graphics toolkits.

     See also: *note graphics_toolkit: XREFgraphics_toolkit, *note
     register_graphics_toolkit: XREFregister_graphics_toolkit.

 -- Built-in Function: loaded_graphics_toolkits ()
     Return a cell array of the currently loaded graphics toolkits.

     See also: *note available_graphics_toolkits:
     XREFavailable_graphics_toolkits.

 -- Built-in Function: register_graphics_toolkit (TOOLKIT)
     List TOOLKIT as an available graphics toolkit.

     See also: *note available_graphics_toolkits:
     XREFavailable_graphics_toolkits.

* Menu:

* Customizing Toolkit Behavior::


File: octave.info,  Node: Customizing Toolkit Behavior,  Up: Graphics Toolkits

15.4.7.1 Customizing Toolkit Behavior
.....................................

The specific behavior of the backend toolkit may be modified using the
following utility functions.  Note: Not all functions apply to every
graphics toolkit.

 -- Loadable Function: [PROG, ARGS] = gnuplot_binary ()
 -- Loadable Function: [OLD_PROG, OLD_ARGS] = gnuplot_binary (NEW_PROG,
          ARG1, ...)
     Query or set the name of the program invoked by the plot command
     when the graphics toolkit is set to "gnuplot".  Additional
     arguments to pass to the external plotting program may also be
     given.  The default value is "gnuplot" with no additional
     arguments.  *Note Installation::.

     See also: *note graphics_toolkit: XREFgraphics_toolkit.


File: octave.info,  Node: Matrix Manipulation,  Next: Arithmetic,  Prev: Plotting,  Up: Top

16 Matrix Manipulation
**********************

There are a number of functions available for checking to see if the
elements of a matrix meet some condition, and for rearranging the
elements of a matrix.  For example, Octave can easily tell you if all
the elements of a matrix are finite, or are less than some specified
value.  Octave can also rotate the elements, extract the upper- or
lower-triangular parts, or sort the columns of a matrix.

* Menu:

* Finding Elements and Checking Conditions::
* Rearranging Matrices::
* Special Utility Matrices::
* Famous Matrices::


File: octave.info,  Node: Finding Elements and Checking Conditions,  Next: Rearranging Matrices,  Up: Matrix Manipulation

16.1 Finding Elements and Checking Conditions
=============================================

The functions 'any' and 'all' are useful for determining whether any or
all of the elements of a matrix satisfy some condition.  The 'find'
function is also useful in determining which elements of a matrix meet a
specified condition.

 -- Built-in Function: any (X)
 -- Built-in Function: any (X, DIM)
     For a vector argument, return true (logical 1) if any element of
     the vector is nonzero.

     For a matrix argument, return a row vector of logical ones and
     zeros with each element indicating whether any of the elements of
     the corresponding column of the matrix are nonzero.  For example:

          any (eye (2, 4))
           => [ 1, 1, 0, 0 ]

     If the optional argument DIM is supplied, work along dimension DIM.
     For example:

          any (eye (2, 4), 2)
           => [ 1; 1 ]

     See also: *note all: XREFall.

 -- Built-in Function: all (X)
 -- Built-in Function: all (X, DIM)
     For a vector argument, return true (logical 1) if all elements of
     the vector are nonzero.

     For a matrix argument, return a row vector of logical ones and
     zeros with each element indicating whether all of the elements of
     the corresponding column of the matrix are nonzero.  For example:

          all ([2, 3; 1, 0])
              => [ 1, 0 ]

     If the optional argument DIM is supplied, work along dimension DIM.

     See also: *note any: XREFany.

   Since the comparison operators (*note Comparison Ops::) return
matrices of ones and zeros, it is easy to test a matrix for many things,
not just whether the elements are nonzero.  For example,

     all (all (rand (5) < 0.9))
          => 0

tests a random 5 by 5 matrix to see if all of its elements are less than
0.9.

   Note that in conditional contexts (like the test clause of 'if' and
'while' statements) Octave treats the test as if you had typed 'all (all
(condition))'.

 -- Function File: Z = xor (X, Y)
 -- Function File: Z = xor (X1, X2, ...)
     Return the "exclusive or" of X and Y.

     For boolean expressions X and Y, 'xor (X, Y)' is true if and only
     if one of X or Y is true.  Otherwise, if X and Y are both true or
     both false, 'xor' returns false.

     The truth table for the xor operation is

                                      X  Y    Z
                                      -  -    -
                                      0  0    0
                                      1  0    1
                                      0  1    1
                                      1  1    0

     If more than two arguments are given the xor operation is applied
     cumulatively from left to right:

          (...((x1 XOR x2) XOR x3) XOR ...)

     See also: *note and: XREFand, *note or: XREFor, *note not: XREFnot.

 -- Built-in Function: diff (X)
 -- Built-in Function: diff (X, K)
 -- Built-in Function: diff (X, K, DIM)
     If X is a vector of length n, 'diff (X)' is the vector of first
     differences X(2) - X(1), ..., X(n) - X(n-1).

     If X is a matrix, 'diff (X)' is the matrix of column differences
     along the first non-singleton dimension.

     The second argument is optional.  If supplied, 'diff (X, K)', where
     K is a non-negative integer, returns the K-th differences.  It is
     possible that K is larger than the first non-singleton dimension of
     the matrix.  In this case, 'diff' continues to take the differences
     along the next non-singleton dimension.

     The dimension along which to take the difference can be explicitly
     stated with the optional variable DIM.  In this case the K-th order
     differences are calculated along this dimension.  In the case where
     K exceeds 'size (X, DIM)' an empty matrix is returned.

     See also: *note sort: XREFsort, *note merge: XREFmerge.

 -- Mapping Function: isinf (X)
     Return a logical array which is true where the elements of X are
     are infinite and false where they are not.  For example:

          isinf ([13, Inf, NA, NaN])
                => [ 0, 1, 0, 0 ]

     See also: *note isfinite: XREFisfinite, *note isnan: XREFisnan,
     *note isna: XREFisna.

 -- Mapping Function: isnan (X)
     Return a logical array which is true where the elements of X are
     NaN values and false where they are not.  NA values are also
     considered NaN values.  For example:

          isnan ([13, Inf, NA, NaN])
                => [ 0, 0, 1, 1 ]

     See also: *note isna: XREFisna, *note isinf: XREFisinf, *note
     isfinite: XREFisfinite.

 -- Mapping Function: isfinite (X)
     Return a logical array which is true where the elements of X are
     finite values and false where they are not.

     For example:

          isfinite ([13, Inf, NA, NaN])
               => [ 1, 0, 0, 0 ]

     See also: *note isinf: XREFisinf, *note isnan: XREFisnan, *note
     isna: XREFisna.

 -- Function File: [ERR, Y1, ...] = common_size (X1, ...)
     Determine if all input arguments are either scalar or of common
     size.  If so, ERR is zero, and YI is a matrix of the common size
     with all entries equal to XI if this is a scalar or XI otherwise.
     If the inputs cannot be brought to a common size, ERR is 1, and YI
     is XI.  For example:

          [errorcode, a, b] = common_size ([1 2; 3 4], 5)
               => errorcode = 0
               => a = [ 1, 2; 3, 4 ]
               => b = [ 5, 5; 5, 5 ]

     This is useful for implementing functions where arguments can
     either be scalars or of common size.

 -- Built-in Function: IDX = find (X)
 -- Built-in Function: IDX = find (X, N)
 -- Built-in Function: IDX = find (X, N, DIRECTION)
 -- Built-in Function: [i, j] = find (...)
 -- Built-in Function: [i, j, v] = find (...)
     Return a vector of indices of nonzero elements of a matrix, as a
     row if X is a row vector or as a column otherwise.  To obtain a
     single index for each matrix element, Octave pretends that the
     columns of a matrix form one long vector (like Fortran arrays are
     stored).  For example:

          find (eye (2))
            => [ 1; 4 ]

     If two outputs are requested, 'find' returns the row and column
     indices of nonzero elements of a matrix.  For example:

          [i, j] = find (2 * eye (2))
              => i = [ 1; 2 ]
              => j = [ 1; 2 ]

     If three outputs are requested, 'find' also returns a vector
     containing the nonzero values.  For example:

          [i, j, v] = find (3 * eye (2))
                 => i = [ 1; 2 ]
                 => j = [ 1; 2 ]
                 => v = [ 3; 3 ]

     If two inputs are given, N indicates the maximum number of elements
     to find from the beginning of the matrix or vector.

     If three inputs are given, DIRECTION should be one of "first" or
     "last", requesting only the first or last N indices, respectively.
     However, the indices are always returned in ascending order.

     Note that this function is particularly useful for sparse matrices,
     as it extracts the nonzero elements as vectors, which can then be
     used to create the original matrix.  For example:

          sz = size (a);
          [i, j, v] = find (a);
          b = sparse (i, j, v, sz(1), sz(2));

     See also: *note nonzeros: XREFnonzeros.

 -- Built-in Function: IDX = lookup (TABLE, Y)
 -- Built-in Function: IDX = lookup (TABLE, Y, OPT)
     Lookup values in a sorted table.  Usually used as a prelude to
     interpolation.

     If table is increasing and 'idx = lookup (table, y)', then
     'table(idx(i)) <= y(i) < table(idx(i+1))' for all 'y(i)' within the
     table.  If 'y(i) < table(1)' then 'idx(i)' is 0.  If 'y(i) >=
     table(end)' or 'isnan (y(i))' then 'idx(i)' is 'n'.

     If the table is decreasing, then the tests are reversed.  For
     non-strictly monotonic tables, empty intervals are always skipped.
     The result is undefined if TABLE is not monotonic, or if TABLE
     contains a NaN.

     The complexity of the lookup is O(M*log(N)) where N is the size of
     TABLE and M is the size of Y.  In the special case when Y is also
     sorted, the complexity is O(min(M*log(N),M+N)).

     TABLE and Y can also be cell arrays of strings (or Y can be a
     single string).  In this case, string lookup is performed using
     lexicographical comparison.

     If OPTS is specified, it must be a string with letters indicating
     additional options.

     'm'
          'table(idx(i)) == val(i)' if 'val(i)' occurs in table;
          otherwise, 'idx(i)' is zero.

     'b'
          'idx(i)' is a logical 1 or 0, indicating whether 'val(i)' is
          contained in table or not.

     'l'
          For numeric lookups the leftmost subinterval shall be extended
          to infinity (i.e., all indices at least 1)

     'r'
          For numeric lookups the rightmost subinterval shall be
          extended to infinity (i.e., all indices at most n-1).

   If you wish to check if a variable exists at all, instead of
properties its elements may have, consult *note Status of Variables::.


File: octave.info,  Node: Rearranging Matrices,  Next: Special Utility Matrices,  Prev: Finding Elements and Checking Conditions,  Up: Matrix Manipulation

16.2 Rearranging Matrices
=========================

 -- Function File: fliplr (X)
     Flip array left to right.

     Return a copy of X with the order of the columns reversed.  In
     other words, X is flipped left-to-right about a vertical axis.  For
     example:

          fliplr ([1, 2; 3, 4])
               =>  2  1
                   4  3

     See also: *note flipud: XREFflipud, *note flip: XREFflip, *note
     rot90: XREFrot90, *note rotdim: XREFrotdim.

 -- Function File: flipud (X)
     Flip array upside down.

     Return a copy of X with the order of the rows reversed.  In other
     words, X is flipped upside-down about a horizontal axis.  For
     example:

          flipud ([1, 2; 3, 4])
               =>  3  4
                   1  2

     See also: *note fliplr: XREFfliplr, *note flip: XREFflip, *note
     rot90: XREFrot90, *note rotdim: XREFrotdim.

 -- Function File: flip (X)
 -- Function File: flip (X, DIM)
     Flip array across specific dimension.

     Return a copy of X flipped about the dimension DIM.  DIM defaults
     to the first non-singleton dimension.  For example:

          flip ([1  2  3  4])
                =>  4  3  2  1

          flip ([1; 2; 3; 4])
                =>  4
                    3
                    2
                    1

          flip ([1 2; 3 4])
                =>  3  4
                    1  2

          flip ([1 2; 3 4], 2)
                =>  2  1
                    4  3

     See also: *note fliplr: XREFfliplr, *note flipud: XREFflipud, *note
     rot90: XREFrot90, *note rotdim: XREFrotdim, *note permute:
     XREFpermute, *note transpose: XREFtranspose.

 -- Function File: rot90 (A)
 -- Function File: rot90 (A, K)
     Rotate array by 90 degree increments.

     Return a copy of A with the elements rotated counterclockwise in
     90-degree increments.  The second argument is optional, and
     specifies how many 90-degree rotations are to be applied (the
     default value is 1).  Negative values of K rotate the matrix in a
     clockwise direction.  For example,

          rot90 ([1, 2; 3, 4], -1)
              =>  3  1
                  4  2

     rotates the given matrix clockwise by 90 degrees.  The following
     are all equivalent statements:

          rot90 ([1, 2; 3, 4], -1)
          rot90 ([1, 2; 3, 4], 3)
          rot90 ([1, 2; 3, 4], 7)

     The rotation is always performed on the plane of the first two
     dimensions, i.e., rows and columns.  To perform a rotation on any
     other plane, use 'rotdim'.

     See also: *note rotdim: XREFrotdim, *note fliplr: XREFfliplr, *note
     flipud: XREFflipud, *note flip: XREFflip.

 -- Function File: rotdim (X)
 -- Function File: rotdim (X, N)
 -- Function File: rotdim (X, N, PLANE)
     Return a copy of X with the elements rotated counterclockwise in
     90-degree increments.  The second argument N is optional, and
     specifies how many 90-degree rotations are to be applied (the
     default value is 1).  The third argument is also optional and
     defines the plane of the rotation.  If present, PLANE is a two
     element vector containing two different valid dimensions of the
     matrix.  When PLANE is not given the first two non-singleton
     dimensions are used.

     Negative values of N rotate the matrix in a clockwise direction.
     For example,

          rotdim ([1, 2; 3, 4], -1, [1, 2])
               =>  3  1
                   4  2

     rotates the given matrix clockwise by 90 degrees.  The following
     are all equivalent statements:

          rotdim ([1, 2; 3, 4], -1, [1, 2])
          rotdim ([1, 2; 3, 4], 3, [1, 2])
          rotdim ([1, 2; 3, 4], 7, [1, 2])

     See also: *note rot90: XREFrot90, *note fliplr: XREFfliplr, *note
     flipud: XREFflipud, *note flip: XREFflip.

 -- Built-in Function: cat (DIM, ARRAY1, ARRAY2, ..., ARRAYN)
     Return the concatenation of N-D array objects, ARRAY1, ARRAY2, ...,
     ARRAYN along dimension DIM.

          A = ones (2, 2);
          B = zeros (2, 2);
          cat (2, A, B)
            => 1 1 0 0
               1 1 0 0

     Alternatively, we can concatenate A and B along the second
     dimension in the following way:

          [A, B]

     DIM can be larger than the dimensions of the N-D array objects and
     the result will thus have DIM dimensions as the following example
     shows:

          cat (4, ones (2, 2), zeros (2, 2))
            => ans(:,:,1,1) =

                 1 1
                 1 1

               ans(:,:,1,2) =

                 0 0
                 0 0

     See also: *note horzcat: XREFhorzcat, *note vertcat: XREFvertcat.

 -- Built-in Function: horzcat (ARRAY1, ARRAY2, ..., ARRAYN)
     Return the horizontal concatenation of N-D array objects, ARRAY1,
     ARRAY2, ..., ARRAYN along dimension 2.

     Arrays may also be concatenated horizontally using the syntax for
     creating new matrices.  For example:

          HCAT = [ ARRAY1, ARRAY2, ... ]

     See also: *note cat: XREFcat, *note vertcat: XREFvertcat.

 -- Built-in Function: vertcat (ARRAY1, ARRAY2, ..., ARRAYN)
     Return the vertical concatenation of N-D array objects, ARRAY1,
     ARRAY2, ..., ARRAYN along dimension 1.

     Arrays may also be concatenated vertically using the syntax for
     creating new matrices.  For example:

          VCAT = [ ARRAY1; ARRAY2; ... ]

     See also: *note cat: XREFcat, *note horzcat: XREFhorzcat.

 -- Built-in Function: permute (A, PERM)
     Return the generalized transpose for an N-D array object A.  The
     permutation vector PERM must contain the elements '1:ndims (A)' (in
     any order, but each element must appear only once).

     The Nth dimension of A gets remapped to dimension 'PERM(N)'.  For
     example:

          X = zeros ([2, 3, 5, 7]);
          size (X)
             =>  2   3   5   7

          size (permute (X, [2, 1, 3, 4]))
             =>  3   2   5   7

          size (permute (X, [1, 3, 4, 2]))
             =>  2   5   7   3

          ## The identity permutation
          size (permute (X, [1, 2, 3, 4]))
             =>  2   3   5   7

     See also: *note ipermute: XREFipermute.

 -- Built-in Function: ipermute (A, IPERM)
     The inverse of the 'permute' function.  The expression

          ipermute (permute (A, perm), perm)

     returns the original array A.

     See also: *note permute: XREFpermute.

 -- Built-in Function: reshape (A, M, N, ...)
 -- Built-in Function: reshape (A, [M N ...])
 -- Built-in Function: reshape (A, ..., [], ...)
 -- Built-in Function: reshape (A, SIZE)
     Return a matrix with the specified dimensions (M, N, ...) whose
     elements are taken from the matrix A.  The elements of the matrix
     are accessed in column-major order (like Fortran arrays are
     stored).

     The following code demonstrates reshaping a 1x4 row vector into a
     2x2 square matrix.

          reshape ([1, 2, 3, 4], 2, 2)
                =>  1  3
                    2  4

     Note that the total number of elements in the original matrix
     ('prod (size (A))') must match the total number of elements in the
     new matrix ('prod ([M N ...])').

     A single dimension of the return matrix may be left unspecified and
     Octave will determine its size automatically.  An empty matrix ([])
     is used to flag the unspecified dimension.

     See also: *note resize: XREFresize, *note vec: XREFvec, *note
     postpad: XREFpostpad, *note cat: XREFcat, *note squeeze:
     XREFsqueeze.

 -- Built-in Function: resize (X, M)
 -- Built-in Function: resize (X, M, N, ...)
 -- Built-in Function: resize (X, [M N ...])
     Resize X cutting off elements as necessary.

     In the result, element with certain indices is equal to the
     corresponding element of X if the indices are within the bounds of
     X; otherwise, the element is set to zero.

     In other words, the statement

          y = resize (x, dv)

     is equivalent to the following code:

          y = zeros (dv, class (x));
          sz = min (dv, size (x));
          for i = 1:length (sz)
            idx{i} = 1:sz(i);
          endfor
          y(idx{:}) = x(idx{:});

     but is performed more efficiently.

     If only M is supplied, and it is a scalar, the dimension of the
     result is M-by-M.  If M, N, ... are all scalars, then the
     dimensions of the result are M-by-N-by-....  If given a vector as
     input, then the dimensions of the result are given by the elements
     of that vector.

     An object can be resized to more dimensions than it has; in such
     case the missing dimensions are assumed to be 1.  Resizing an
     object to fewer dimensions is not possible.

     See also: *note reshape: XREFreshape, *note postpad: XREFpostpad,
     *note prepad: XREFprepad, *note cat: XREFcat.

 -- Function File: Y = circshift (X, N)
     Circularly shift the values of the array X.  N must be a vector of
     integers no longer than the number of dimensions in X.  The values
     of N can be either positive or negative, which determines the
     direction in which the values or X are shifted.  If an element of N
     is zero, then the corresponding dimension of X will not be shifted.
     For example:

          x = [1, 2, 3; 4, 5, 6; 7, 8, 9];
          circshift (x, 1)
          =>  7, 8, 9
              1, 2, 3
              4, 5, 6
          circshift (x, -2)
          =>  7, 8, 9
              1, 2, 3
              4, 5, 6
          circshift (x, [0,1])
          =>  3, 1, 2
              6, 4, 5
              9, 7, 8

     See also: *note permute: XREFpermute, *note ipermute: XREFipermute,
     *note shiftdim: XREFshiftdim.

 -- Function File: shift (X, B)
 -- Function File: shift (X, B, DIM)
     If X is a vector, perform a circular shift of length B of the
     elements of X.

     If X is a matrix, do the same for each column of X.  If the
     optional DIM argument is given, operate along this dimension.

 -- Function File: Y = shiftdim (X, N)
 -- Function File: [Y, NS] = shiftdim (X)
     Shift the dimensions of X by N, where N must be an integer scalar.
     When N is positive, the dimensions of X are shifted to the left,
     with the leading dimensions circulated to the end.  If N is
     negative, then the dimensions of X are shifted to the right, with N
     leading singleton dimensions added.

     Called with a single argument, 'shiftdim', removes the leading
     singleton dimensions, returning the number of dimensions removed in
     the second output argument NS.

     For example:

          x = ones (1, 2, 3);
          size (shiftdim (x, -1))
             => [1, 1, 2, 3]
          size (shiftdim (x, 1))
             => [2, 3]
          [b, ns] = shiftdim (x)
             => b = [1, 1, 1; 1, 1, 1]
             => ns = 1

     See also: *note reshape: XREFreshape, *note permute: XREFpermute,
     *note ipermute: XREFipermute, *note circshift: XREFcircshift, *note
     squeeze: XREFsqueeze.

 -- Built-in Function: [S, I] = sort (X)
 -- Built-in Function: [S, I] = sort (X, DIM)
 -- Built-in Function: [S, I] = sort (X, MODE)
 -- Built-in Function: [S, I] = sort (X, DIM, MODE)
     Return a copy of X with the elements arranged in increasing order.
     For matrices, 'sort' orders the elements within columns

     For example:

          sort ([1, 2; 2, 3; 3, 1])
             =>  1  1
                 2  2
                 3  3

     If the optional argument DIM is given, then the matrix is sorted
     along the dimension defined by DIM.  The optional argument 'mode'
     defines the order in which the values will be sorted.  Valid values
     of 'mode' are "ascend" or "descend".

     The 'sort' function may also be used to produce a matrix containing
     the original row indices of the elements in the sorted matrix.  For
     example:

          [s, i] = sort ([1, 2; 2, 3; 3, 1])
            => s = 1  1
                   2  2
                   3  3
            => i = 1  3
                   2  1
                   3  2

     For equal elements, the indices are such that equal elements are
     listed in the order in which they appeared in the original list.

     Sorting of complex entries is done first by magnitude ('abs (Z)')
     and for any ties by phase angle ('angle (z)').  For example:

          sort ([1+i; 1; 1-i])
              => 1 + 0i
                 1 - 1i
                 1 + 1i

     NaN values are treated as being greater than any other value and
     are sorted to the end of the list.

     The 'sort' function may also be used to sort strings and cell
     arrays of strings, in which case ASCII dictionary order (uppercase
     'A' precedes lowercase 'a') of the strings is used.

     The algorithm used in 'sort' is optimized for the sorting of
     partially ordered lists.

     See also: *note sortrows: XREFsortrows, *note issorted:
     XREFissorted.

 -- Function File: [S, I] = sortrows (A)
 -- Function File: [S, I] = sortrows (A, C)
     Sort the rows of the matrix A according to the order of the columns
     specified in C.  If C is omitted, a lexicographical sort is used.
     By default ascending order is used however if elements of C are
     negative then the corresponding column is sorted in descending
     order.

     See also: *note sort: XREFsort.

 -- Built-in Function: issorted (A)
 -- Built-in Function: issorted (A, MODE)
 -- Built-in Function: issorted (A, "rows", MODE)
     Return true if the array is sorted according to MODE, which may be
     either "ascending", "descending", or "either".  By default, MODE is
     "ascending".  NaNs are treated in the same manner as 'sort'.

     If the optional argument "rows" is supplied, check whether the
     array is sorted by rows as output by the function 'sortrows' (with
     no options).

     This function does not support sparse matrices.

     See also: *note sort: XREFsort, *note sortrows: XREFsortrows.

 -- Built-in Function: nth_element (X, N)
 -- Built-in Function: nth_element (X, N, DIM)
     Select the n-th smallest element of a vector, using the ordering
     defined by 'sort'.  In other words, the result is equivalent to
     'sort(X)(N)'.  N can also be a contiguous range, either ascending
     'l:u' or descending 'u:-1:l', in which case a range of elements is
     returned.  If X is an array, 'nth_element' operates along the
     dimension defined by DIM, or the first non-singleton dimension if
     DIM is not given.

     nth_element encapsulates the C++ standard library algorithms
     nth_element and partial_sort.  On average, the complexity of the
     operation is O(M*log(K)), where 'M = size (X, DIM)' and
     'K = length (N)'.  This function is intended for cases where the
     ratio K/M is small; otherwise, it may be better to use 'sort'.

     See also: *note sort: XREFsort, *note min: XREFmin, *note max:
     XREFmax.

 -- Function File: tril (A)
 -- Function File: tril (A, K)
 -- Function File: tril (A, K, PACK)
 -- Function File: triu (A)
 -- Function File: triu (A, K)
 -- Function File: triu (A, K, PACK)
     Return a new matrix formed by extracting the lower ('tril') or
     upper ('triu') triangular part of the matrix A, and setting all
     other elements to zero.  The second argument is optional, and
     specifies how many diagonals above or below the main diagonal
     should also be set to zero.

     The default value of K is zero, so that 'triu' and 'tril' normally
     include the main diagonal as part of the result.

     If the value of K is nonzero integer, the selection of elements
     starts at an offset of K diagonals above or below the main
     diagonal; above for positive K and below for negative K.

     The absolute value of K must not be greater than the number of
     subdiagonals or superdiagonals.

     For example:

          tril (ones (3), -1)
               =>  0  0  0
                   1  0  0
                   1  1  0

     and

          tril (ones (3), 1)
               =>  1  1  0
                   1  1  1
                   1  1  1

     If the option "pack" is given as third argument, the extracted
     elements are not inserted into a matrix, but rather stacked
     column-wise one above other.

     See also: *note diag: XREFdiag.

 -- Built-in Function: V = vec (X)
 -- Built-in Function: V = vec (X, DIM)
     Return the vector obtained by stacking the columns of the matrix X
     one above the other.  Without DIM this is equivalent to 'X(:)'.  If
     DIM is supplied, the dimensions of V are set to DIM with all
     elements along the last dimension.  This is equivalent to 'shiftdim
     (X(:), 1-DIM)'.

     See also: *note vech: XREFvech, *note resize: XREFresize, *note
     cat: XREFcat.

 -- Function File: vech (X)
     Return the vector obtained by eliminating all superdiagonal
     elements of the square matrix X and stacking the result one column
     above the other.  This has uses in matrix calculus where the
     underlying matrix is symmetric and it would be pointless to keep
     values above the main diagonal.

     See also: *note vec: XREFvec.

 -- Function File: prepad (X, L)
 -- Function File: prepad (X, L, C)
 -- Function File: prepad (X, L, C, DIM)
     Prepend the scalar value C to the vector X until it is of length L.
     If C is not given, a value of 0 is used.

     If 'length (X) > L', elements from the beginning of X are removed
     until a vector of length L is obtained.

     If X is a matrix, elements are prepended or removed from each row.

     If the optional argument DIM is given, operate along this
     dimension.

     If DIM is larger than the dimensions of X, the result will have DIM
     dimensions.

     See also: *note postpad: XREFpostpad, *note cat: XREFcat, *note
     resize: XREFresize.

 -- Function File: postpad (X, L)
 -- Function File: postpad (X, L, C)
 -- Function File: postpad (X, L, C, DIM)
     Append the scalar value C to the vector X until it is of length L.
     If C is not given, a value of 0 is used.

     If 'length (X) > L', elements from the end of X are removed until a
     vector of length L is obtained.

     If X is a matrix, elements are appended or removed from each row.

     If the optional argument DIM is given, operate along this
     dimension.

     If DIM is larger than the dimensions of X, the result will have DIM
     dimensions.

     See also: *note prepad: XREFprepad, *note cat: XREFcat, *note
     resize: XREFresize.

 -- Built-in Function: M = diag (V)
 -- Built-in Function: M = diag (V, K)
 -- Built-in Function: M = diag (V, M, N)
 -- Built-in Function: V = diag (M)
 -- Built-in Function: V = diag (M, K)
     Return a diagonal matrix with vector V on diagonal K.  The second
     argument is optional.  If it is positive, the vector is placed on
     the K-th superdiagonal.  If it is negative, it is placed on the
     -K-th subdiagonal.  The default value of K is 0, and the vector is
     placed on the main diagonal.  For example:

          diag ([1, 2, 3], 1)
             =>  0  1  0  0
                 0  0  2  0
                 0  0  0  3
                 0  0  0  0

     The 3-input form returns a diagonal matrix with vector V on the
     main diagonal and the resulting matrix being of size M rows x N
     columns.

     Given a matrix argument, instead of a vector, 'diag' extracts the
     K-th diagonal of the matrix.

 -- Function File: blkdiag (A, B, C, ...)
     Build a block diagonal matrix from A, B, C, ... All the arguments
     must be numeric and are two-dimensional matrices or scalars.  If
     any argument is of type sparse, the output will also be sparse.

     See also: *note diag: XREFdiag, *note horzcat: XREFhorzcat, *note
     vertcat: XREFvertcat, *note sparse: XREFsparse.


File: octave.info,  Node: Special Utility Matrices,  Next: Famous Matrices,  Prev: Rearranging Matrices,  Up: Matrix Manipulation

16.3 Special Utility Matrices
=============================

 -- Built-in Function: eye (N)
 -- Built-in Function: eye (M, N)
 -- Built-in Function: eye ([M N])
 -- Built-in Function: eye (..., CLASS)
     Return an identity matrix.  If invoked with a single scalar
     argument N, return a square NxN identity matrix.  If supplied two
     scalar arguments (M, N), 'eye' takes them to be the number of rows
     and columns.  If given a vector with two elements, 'eye' uses the
     values of the elements as the number of rows and columns,
     respectively.  For example:

          eye (3)
           =>  1  0  0
               0  1  0
               0  0  1

     The following expressions all produce the same result:

          eye (2)
          ==
          eye (2, 2)
          ==
          eye (size ([1, 2; 3, 4]))

     The optional argument CLASS, allows 'eye' to return an array of the
     specified type, like

          val = zeros (n,m, "uint8")

     Calling 'eye' with no arguments is equivalent to calling it with an
     argument of 1.  Any negative dimensions are treated as zero.  These
     odd definitions are for compatibility with MATLAB.

     See also: *note speye: XREFspeye, *note ones: XREFones, *note
     zeros: XREFzeros.

 -- Built-in Function: ones (N)
 -- Built-in Function: ones (M, N)
 -- Built-in Function: ones (M, N, K, ...)
 -- Built-in Function: ones ([M N ...])
 -- Built-in Function: ones (..., CLASS)
     Return a matrix or N-dimensional array whose elements are all 1.
     If invoked with a single scalar integer argument N, return a square
     NxN matrix.  If invoked with two or more scalar integer arguments,
     or a vector of integer values, return an array with the given
     dimensions.

     If you need to create a matrix whose values are all the same, you
     should use an expression like

          val_matrix = val * ones (m, n)

     The optional argument CLASS specifies the class of the return array
     and defaults to double.  For example:

          val = ones (m,n, "uint8")

     See also: *note zeros: XREFzeros.

 -- Built-in Function: zeros (N)
 -- Built-in Function: zeros (M, N)
 -- Built-in Function: zeros (M, N, K, ...)
 -- Built-in Function: zeros ([M N ...])
 -- Built-in Function: zeros (..., CLASS)
     Return a matrix or N-dimensional array whose elements are all 0.
     If invoked with a single scalar integer argument, return a square
     NxN matrix.  If invoked with two or more scalar integer arguments,
     or a vector of integer values, return an array with the given
     dimensions.

     The optional argument CLASS specifies the class of the return array
     and defaults to double.  For example:

          val = zeros (m,n, "uint8")

     See also: *note ones: XREFones.

 -- Function File: repmat (A, M)
 -- Function File: repmat (A, M, N)
 -- Function File: repmat (A, M, N, P ...)
 -- Function File: repmat (A, [M N])
 -- Function File: repmat (A, [M N P ...])
     Form a block matrix of size M by N, with a copy of matrix A as each
     element.  If N is not specified, form an M by M block matrix.  For
     copying along more than two dimensions, specify the number of times
     to copy across each dimension M, N, P, ..., in a vector in the
     second argument.

     See also: *note repelems: XREFrepelems.

 -- Built-in Function: repelems (X, R)
     Construct a vector of repeated elements from X.  R is a 2xN integer
     matrix specifying which elements to repeat and how often to repeat
     each element.

     Entries in the first row, R(1,j), select an element to repeat.  The
     corresponding entry in the second row, R(2,j), specifies the repeat
     count.  If X is a matrix then the columns of X are imagined to be
     stacked on top of each other for purposes of the selection index.
     A row vector is always returned.

     Conceptually the result is calculated as follows:

          y = [];
          for i = 1:columns (R)
            y = [y, X(R(1,i)*ones(1, R(2,i)))];
          endfor

     See also: *note repmat: XREFrepmat, *note cat: XREFcat.

   The functions 'linspace' and 'logspace' make it very easy to create
vectors with evenly or logarithmically spaced elements.  *Note Ranges::.

 -- Built-in Function: linspace (BASE, LIMIT)
 -- Built-in Function: linspace (BASE, LIMIT, N)
     Return a row vector with N linearly spaced elements between BASE
     and LIMIT.  If the number of elements is greater than one, then the
     endpoints BASE and LIMIT are always included in the range.  If BASE
     is greater than LIMIT, the elements are stored in decreasing order.
     If the number of points is not specified, a value of 100 is used.

     The 'linspace' function always returns a row vector if both BASE
     and LIMIT are scalars.  If one, or both, of them are column
     vectors, 'linspace' returns a matrix.

     For compatibility with MATLAB, return the second argument (LIMIT)
     if fewer than two values are requested.

     See also: *note logspace: XREFlogspace.

 -- Function File: logspace (A, B)
 -- Function File: logspace (A, B, N)
 -- Function File: logspace (A, pi, N)
     Return a row vector with N elements logarithmically spaced from
     10^A to 10^B.  If N is unspecified it defaults to 50.

     If B is equal to pi, the points are between 10^A and pi, _not_ 10^A
     and 10^pi, in order to be compatible with the corresponding MATLAB
     function.

     Also for compatibility with MATLAB, return the second argument B if
     fewer than two values are requested.

     See also: *note linspace: XREFlinspace.

 -- Built-in Function: rand (N)
 -- Built-in Function: rand (M, N, ...)
 -- Built-in Function: rand ([M N ...])
 -- Built-in Function: V = rand ("state")
 -- Built-in Function: rand ("state", V)
 -- Built-in Function: rand ("state", "reset")
 -- Built-in Function: V = rand ("seed")
 -- Built-in Function: rand ("seed", V)
 -- Built-in Function: rand ("seed", "reset")
 -- Built-in Function: rand (..., "single")
 -- Built-in Function: rand (..., "double")
     Return a matrix with random elements uniformly distributed on the
     interval (0, 1).  The arguments are handled the same as the
     arguments for 'eye'.

     You can query the state of the random number generator using the
     form

          v = rand ("state")

     This returns a column vector V of length 625.  Later, you can
     restore the random number generator to the state V using the form

          rand ("state", v)

     You may also initialize the state vector from an arbitrary vector
     of length <= 625 for V.  This new state will be a hash based on the
     value of V, not V itself.

     By default, the generator is initialized from '/dev/urandom' if it
     is available, otherwise from CPU time, wall clock time, and the
     current fraction of a second.  Note that this differs from MATLAB,
     which always initializes the state to the same state at startup.
     To obtain behavior comparable to MATLAB, initialize with a
     deterministic state vector in Octave's startup files (*note Startup
     Files::).

     To compute the pseudo-random sequence, 'rand' uses the Mersenne
     Twister with a period of 2^{19937}-1 (See M. Matsumoto and T.
     Nishimura, 'Mersenne Twister: A 623-dimensionally equidistributed
     uniform pseudorandom number generator', ACM Trans.  on Modeling and
     Computer Simulation Vol.  8, No.  1, pp.  3-30, January 1998,
     <http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html>).  Do
     *not* use for cryptography without securely hashing several
     returned values together, otherwise the generator state can be
     learned after reading 624 consecutive values.

     Older versions of Octave used a different random number generator.
     The new generator is used by default as it is significantly faster
     than the old generator, and produces random numbers with a
     significantly longer cycle time.  However, in some circumstances it
     might be desirable to obtain the same random sequences as used by
     the old generators.  To do this the keyword "seed" is used to
     specify that the old generators should be use, as in

          rand ("seed", val)

     which sets the seed of the generator to VAL.  The seed of the
     generator can be queried with

          s = rand ("seed")

     However, it should be noted that querying the seed will not cause
     'rand' to use the old generators, only setting the seed will.  To
     cause 'rand' to once again use the new generators, the keyword
     "state" should be used to reset the state of the 'rand'.

     The state or seed of the generator can be reset to a new random
     value using the "reset" keyword.

     The class of the value returned can be controlled by a trailing
     "double" or "single" argument.  These are the only valid classes.

     See also: *note randn: XREFrandn, *note rande: XREFrande, *note
     randg: XREFrandg, *note randp: XREFrandp.

 -- Function File: randi (IMAX)
 -- Function File: randi (IMAX, N)
 -- Function File: randi (IMAX, M, N, ...)
 -- Function File: randi ([IMIN IMAX], ...)
 -- Function File: randi (..., "CLASS")
     Return random integers in the range 1:IMAX.

     Additional arguments determine the shape of the return matrix.
     When no arguments are specified a single random integer is
     returned.  If one argument N is specified then a square matrix (N x N)
     is returned.  Two or more arguments will return a multi-dimensional
     matrix (M x N x ...).

     The integer range may optionally be described by a two element
     matrix with a lower and upper bound in which case the returned
     integers will be on the interval [IMIN, IMAX].

     The optional argument CLASS will return a matrix of the requested
     type.  The default is "double".

     The following example returns 150 integers in the range 1-10.

          ri = randi (10, 150, 1)

     Implementation Note: 'randi' relies internally on 'rand' which uses
     class "double" to represent numbers.  This limits the maximum
     integer (IMAX) and range (IMAX - IMIN) to the value returned by the
     'bitmax' function.  For IEEE floating point numbers this value is
     2^{53} - 1.

     See also: *note rand: XREFrand.

 -- Built-in Function: randn (N)
 -- Built-in Function: randn (M, N, ...)
 -- Built-in Function: randn ([M N ...])
 -- Built-in Function: V = randn ("state")
 -- Built-in Function: randn ("state", V)
 -- Built-in Function: randn ("state", "reset")
 -- Built-in Function: V = randn ("seed")
 -- Built-in Function: randn ("seed", V)
 -- Built-in Function: randn ("seed", "reset")
 -- Built-in Function: randn (..., "single")
 -- Built-in Function: randn (..., "double")
     Return a matrix with normally distributed random elements having
     zero mean and variance one.  The arguments are handled the same as
     the arguments for 'rand'.

     By default, 'randn' uses the Marsaglia and Tsang "Ziggurat
     technique" to transform from a uniform to a normal distribution.

     The class of the value returned can be controlled by a trailing
     "double" or "single" argument.  These are the only valid classes.

     Reference: G. Marsaglia and W.W. Tsang, 'Ziggurat Method for
     Generating Random Variables', J. Statistical Software, vol 5, 2000,
     <http://www.jstatsoft.org/v05/i08/>

     See also: *note rand: XREFrand, *note rande: XREFrande, *note
     randg: XREFrandg, *note randp: XREFrandp.

 -- Built-in Function: rande (N)
 -- Built-in Function: rande (M, N, ...)
 -- Built-in Function: rande ([M N ...])
 -- Built-in Function: V = rande ("state")
 -- Built-in Function: rande ("state", V)
 -- Built-in Function: rande ("state", "reset")
 -- Built-in Function: V = rande ("seed")
 -- Built-in Function: rande ("seed", V)
 -- Built-in Function: rande ("seed", "reset")
 -- Built-in Function: rande (..., "single")
 -- Built-in Function: rande (..., "double")
     Return a matrix with exponentially distributed random elements.
     The arguments are handled the same as the arguments for 'rand'.

     By default, 'randn' uses the Marsaglia and Tsang "Ziggurat
     technique" to transform from a uniform to a normal distribution.

     The class of the value returned can be controlled by a trailing
     "double" or "single" argument.  These are the only valid classes.

     Reference: G. Marsaglia and W.W. Tsang, 'Ziggurat Method for
     Generating Random Variables', J. Statistical Software, vol 5, 2000,
     <http://www.jstatsoft.org/v05/i08/>

     See also: *note rand: XREFrand, *note randn: XREFrandn, *note
     randg: XREFrandg, *note randp: XREFrandp.

 -- Built-in Function: randp (L, N)
 -- Built-in Function: randp (L, M, N, ...)
 -- Built-in Function: randp (L, [M N ...])
 -- Built-in Function: V = randp ("state")
 -- Built-in Function: randp ("state", V)
 -- Built-in Function: randp ("state", "reset")
 -- Built-in Function: V = randp ("seed")
 -- Built-in Function: randp ("seed", V)
 -- Built-in Function: randp ("seed", "reset")
 -- Built-in Function: randp (..., "single")
 -- Built-in Function: randp (..., "double")
     Return a matrix with Poisson distributed random elements with mean
     value parameter given by the first argument, L.  The arguments are
     handled the same as the arguments for 'rand', except for the
     argument L.

     Five different algorithms are used depending on the range of L and
     whether or not L is a scalar or a matrix.

     For scalar L <= 12, use direct method.
          W.H. Press, et al., 'Numerical Recipes in C', Cambridge
          University Press, 1992.

     For scalar L > 12, use rejection method.[1]
          W.H. Press, et al., 'Numerical Recipes in C', Cambridge
          University Press, 1992.

     For matrix L <= 10, use inversion method.[2]
          E. Stadlober, et al., WinRand source code, available via FTP.

     For matrix L > 10, use patchwork rejection method.
          E. Stadlober, et al., WinRand source code, available via FTP,
          or H. Zechner, 'Efficient sampling from continuous and
          discrete unimodal distributions', Doctoral Dissertation,
          156pp., Technical University Graz, Austria, 1994.

     For L > 1e8, use normal approximation.
          L. Montanet, et al., 'Review of Particle Properties', Physical
          Review D 50 p1284, 1994.

     The class of the value returned can be controlled by a trailing
     "double" or "single" argument.  These are the only valid classes.

     See also: *note rand: XREFrand, *note randn: XREFrandn, *note
     rande: XREFrande, *note randg: XREFrandg.

 -- Built-in Function: randg (N)
 -- Built-in Function: randg (M, N, ...)
 -- Built-in Function: randg ([M N ...])
 -- Built-in Function: V = randg ("state")
 -- Built-in Function: randg ("state", V)
 -- Built-in Function: randg ("state", "reset")
 -- Built-in Function: V = randg ("seed")
 -- Built-in Function: randg ("seed", V)
 -- Built-in Function: randg ("seed", "reset")
 -- Built-in Function: randg (..., "single")
 -- Built-in Function: randg (..., "double")
     Return a matrix with 'gamma (A,1)' distributed random elements.
     The arguments are handled the same as the arguments for 'rand',
     except for the argument A.

     This can be used to generate many distributions:

     'gamma (a, b)' for 'a > -1', 'b > 0'

               r = b * randg (a)

     'beta (a, b)' for 'a > -1', 'b > -1'

               r1 = randg (a, 1)
               r = r1 / (r1 + randg (b, 1))

     'Erlang (a, n)'

               r = a * randg (n)

     'chisq (df)' for 'df > 0'

               r = 2 * randg (df / 2)

     't (df)' for '0 < df < inf' (use randn if df is infinite)

               r = randn () / sqrt (2 * randg (df / 2) / df)

     'F (n1, n2)' for '0 < n1', '0 < n2'

               ## r1 equals 1 if n1 is infinite
               r1 = 2 * randg (n1 / 2) / n1
               ## r2 equals 1 if n2 is infinite
               r2 = 2 * randg (n2 / 2) / n2
               r = r1 / r2


     negative 'binomial (n, p)' for 'n > 0', '0 < p <= 1'

               r = randp ((1 - p) / p * randg (n))

     non-central 'chisq (df, L)', for 'df >= 0' and 'L > 0'
          (use chisq if 'L = 0')

               r = randp (L / 2)
               r(r > 0) = 2 * randg (r(r > 0))
               r(df > 0) += 2 * randg (df(df > 0)/2)

     'Dirichlet (a1, ... ak)'

               r = (randg (a1), ..., randg (ak))
               r = r / sum (r)

     The class of the value returned can be controlled by a trailing
     "double" or "single" argument.  These are the only valid classes.

     See also: *note rand: XREFrand, *note randn: XREFrandn, *note
     rande: XREFrande, *note randp: XREFrandp.

   The generators operate in the new or old style together, it is not
possible to mix the two.  Initializing any generator with "state" or
"seed" causes the others to switch to the same style for future calls.

   The state of each generator is independent and calls to different
generators can be interleaved without affecting the final result.  For
example,

     rand ("state", [11, 22, 33]);
     randn ("state", [44, 55, 66]);
     u = rand (100, 1);
     n = randn (100, 1);

and

     rand ("state", [11, 22, 33]);
     randn ("state", [44, 55, 66]);
     u = zeros (100, 1);
     n = zeros (100, 1);
     for i = 1:100
       u(i) = rand ();
       n(i) = randn ();
     end

produce equivalent results.  When the generators are initialized in the
old style with "seed" only 'rand' and 'randn' are independent, because
the old 'rande', 'randg' and 'randp' generators make calls to 'rand' and
'randn'.

   The generators are initialized with random states at start-up, so
that the sequences of random numbers are not the same each time you run
Octave.(1)  If you really do need to reproduce a sequence of numbers
exactly, you can set the state or seed to a specific value.

   If invoked without arguments, 'rand' and 'randn' return a single
element of a random sequence.

   The original 'rand' and 'randn' functions use Fortran code from
RANLIB, a library of Fortran routines for random number generation,
compiled by Barry W. Brown and James Lovato of the Department of
Biomathematics at The University of Texas, M.D. Anderson Cancer Center,
Houston, TX 77030.

 -- Built-in Function: randperm (N)
 -- Built-in Function: randperm (N, M)
     Return a row vector containing a random permutation of '1:N'.  If M
     is supplied, return M unique entries, sampled without replacement
     from '1:N'.  The complexity is O(N) in memory and O(M) in time,
     unless M < N/5, in which case O(M) memory is used as well.  The
     randomization is performed using rand().  All permutations are
     equally likely.

     See also: *note perms: XREFperms.

   ---------- Footnotes ----------

   (1) The old versions of 'rand' and 'randn' obtain their initial seeds
from the system clock.


File: octave.info,  Node: Famous Matrices,  Prev: Special Utility Matrices,  Up: Matrix Manipulation

16.4 Famous Matrices
====================

The following functions return famous matrix forms.

 -- Function File: gallery (NAME)
 -- Function File: gallery (NAME, ARGS)
     Create interesting matrices for testing.

 -- Function File: C = gallery ("cauchy", X)
 -- Function File: C = gallery ("cauchy", X, Y)
     Create a Cauchy matrix.

 -- Function File: C = gallery ("chebspec", N)
 -- Function File: C = gallery ("chebspec", N, K)
     Create a Chebyshev spectral differentiation matrix.

 -- Function File: C = gallery ("chebvand", P)
 -- Function File: C = gallery ("chebvand", M, P)
     Create a Vandermonde-like matrix for the Chebyshev polynomials.

 -- Function File: A = gallery ("chow", N)
 -- Function File: A = gallery ("chow", N, ALPHA)
 -- Function File: A = gallery ("chow", N, ALPHA, DELTA)
     Create a Chow matrix - a singular Toeplitz lower Hessenberg matrix.

 -- Function File: C = gallery ("circul", V)
     Create a circulant matrix.

 -- Function File: A = gallery ("clement", N)
 -- Function File: A = gallery ("clement", N, K)
     Create a tridiagonal matrix with zero diagonal entries.

 -- Function File: C = gallery ("compar", A)
 -- Function File: C = gallery ("compar", A, K)
     Create a comparison matrix.

 -- Function File: A = gallery ("condex", N)
 -- Function File: A = gallery ("condex", N, K)
 -- Function File: A = gallery ("condex", N, K, THETA)
     Create a 'counterexample' matrix to a condition estimator.

 -- Function File: A = gallery ("cycol", [M N])
 -- Function File: A = gallery ("cycol", N)
 -- Function File: A = gallery (..., K)
     Create a matrix whose columns repeat cyclically.

 -- Function File: [C, D, E] = gallery ("dorr", N)
 -- Function File: [C, D, E] = gallery ("dorr", N, THETA)
 -- Function File: A = gallery ("dorr", ...)
     Create a diagonally dominant, ill-conditioned, tridiagonal matrix.

 -- Function File: A = gallery ("dramadah", N)
 -- Function File: A = gallery ("dramadah", N, K)
     Create a (0, 1) matrix whose inverse has large integer entries.

 -- Function File: A = gallery ("fiedler", C)
     Create a symmetric Fiedler matrix.

 -- Function File: A = gallery ("forsythe", N)
 -- Function File: A = gallery ("forsythe", N, ALPHA)
 -- Function File: A = gallery ("forsythe", N, ALPHA, LAMBDA)
     Create a Forsythe matrix (a perturbed Jordan block).

 -- Function File: F = gallery ("frank", N)
 -- Function File: F = gallery ("frank", N, K)
     Create a Frank matrix (ill-conditioned eigenvalues).

 -- Function File: C = gallery ("gcdmat", N)
     Create a greatest common divisor matrix.

     C is an N-by-N matrix whose values correspond to the greatest
     common divisor of its coordinate values, i.e., C(i,j) correspond
     'gcd (i, j)'.

 -- Function File: A = gallery ("gearmat", N)
 -- Function File: A = gallery ("gearmat", N, I)
 -- Function File: A = gallery ("gearmat", N, I, J)
     Create a Gear matrix.

 -- Function File: G = gallery ("grcar", N)
 -- Function File: G = gallery ("grcar", N, K)
     Create a Toeplitz matrix with sensitive eigenvalues.

 -- Function File: A = gallery ("hanowa", N)
 -- Function File: A = gallery ("hanowa", N, D)
     Create a matrix whose eigenvalues lie on a vertical line in the
     complex plane.

 -- Function File: V = gallery ("house", X)
 -- Function File: [V, BETA] = gallery ("house", X)
     Create a householder matrix.

 -- Function File: A = gallery ("integerdata", IMAX, [M N ...], J)
 -- Function File: A = gallery ("integerdata", IMAX, M, N, ..., J)
 -- Function File: A = gallery ("integerdata", [IMIN, IMAX], [M N ...],
          J)
 -- Function File: A = gallery ("integerdata", [IMIN, IMAX], M, N, ...,
          J)
 -- Function File: A = gallery ("integerdata", ..., "CLASS")
     Create a matrix with random integers in the range [1, IMAX].  If
     IMIN is given then the integers are in the range [IMIN, IMAX].

     The second input is a matrix of dimensions describing the size of
     the output.  The dimensions can also be input as comma-separated
     arguments.

     The input J is an integer index in the range [0, 2^32-1].  The
     values of the output matrix are always exactly the same
     (reproducibility) for a given size input and J index.

     The final optional argument determines the class of the resulting
     matrix.  Possible values for CLASS: "uint8", "uint16", "uint32",
     "int8", "int16", int32", "single", "double".  The default is
     "double".

 -- Function File: A = gallery ("invhess", X)
 -- Function File: A = gallery ("invhess", X, Y)
     Create the inverse of an upper Hessenberg matrix.

 -- Function File: A = gallery ("invol", N)
     Create an involutory matrix.

 -- Function File: A = gallery ("ipjfact", N)
 -- Function File: A = gallery ("ipjfact", N, K)
     Create an Hankel matrix with factorial elements.

 -- Function File: A = gallery ("jordbloc", N)
 -- Function File: A = gallery ("jordbloc", N, LAMBDA)
     Create a Jordan block.

 -- Function File: U = gallery ("kahan", N)
 -- Function File: U = gallery ("kahan", N, THETA)
 -- Function File: U = gallery ("kahan", N, THETA, PERT)
     Create a Kahan matrix (upper trapezoidal).

 -- Function File: A = gallery ("kms", N)
 -- Function File: A = gallery ("kms", N, RHO)
     Create a Kac-Murdock-Szego Toeplitz matrix.

 -- Function File: B = gallery ("krylov", A)
 -- Function File: B = gallery ("krylov", A, X)
 -- Function File: B = gallery ("krylov", A, X, J)
     Create a Krylov matrix.

 -- Function File: A = gallery ("lauchli", N)
 -- Function File: A = gallery ("lauchli", N, MU)
     Create a Lauchli matrix (rectangular).

 -- Function File: A = gallery ("lehmer", N)
     Create a Lehmer matrix (symmetric positive definite).

 -- Function File: T = gallery ("lesp", N)
     Create a tridiagonal matrix with real, sensitive eigenvalues.

 -- Function File: A = gallery ("lotkin", N)
     Create a Lotkin matrix.

 -- Function File: A = gallery ("minij", N)
     Create a symmetric positive definite matrix MIN(i,j).

 -- Function File: A = gallery ("moler", N)
 -- Function File: A = gallery ("moler", N, ALPHA)
     Create a Moler matrix (symmetric positive definite).

 -- Function File: [A, T] = gallery ("neumann", N)
     Create a singular matrix from the discrete Neumann problem
     (sparse).

 -- Function File: A = gallery ("normaldata", [M N ...], J)
 -- Function File: A = gallery ("normaldata", M, N, ..., J)
 -- Function File: A = gallery ("normaldata", ..., "CLASS")
     Create a matrix with random samples from the standard normal
     distribution (mean = 0, std = 1).

     The first input is a matrix of dimensions describing the size of
     the output.  The dimensions can also be input as comma-separated
     arguments.

     The input J is an integer index in the range [0, 2^32-1].  The
     values of the output matrix are always exactly the same
     (reproducibility) for a given size input and J index.

     The final optional argument determines the class of the resulting
     matrix.  Possible values for CLASS: "single", "double".  The
     default is "double".

 -- Function File: Q = gallery ("orthog", N)
 -- Function File: Q = gallery ("orthog", N, K)
     Create orthogonal and nearly orthogonal matrices.

 -- Function File: A = gallery ("parter", N)
     Create a Parter matrix (a Toeplitz matrix with singular values near
     pi).

 -- Function File: P = gallery ("pei", N)
 -- Function File: P = gallery ("pei", N, ALPHA)
     Create a Pei matrix.

 -- Function File: A = gallery ("Poisson", N)
     Create a block tridiagonal matrix from Poisson's equation (sparse).

 -- Function File: A = gallery ("prolate", N)
 -- Function File: A = gallery ("prolate", N, W)
     Create a prolate matrix (symmetric, ill-conditioned Toeplitz
     matrix).

 -- Function File: H = gallery ("randhess", X)
     Create a random, orthogonal upper Hessenberg matrix.

 -- Function File: A = gallery ("rando", N)
 -- Function File: A = gallery ("rando", N, K)
     Create a random matrix with elements -1, 0 or 1.

 -- Function File: A = gallery ("randsvd", N)
 -- Function File: A = gallery ("randsvd", N, KAPPA)
 -- Function File: A = gallery ("randsvd", N, KAPPA, MODE)
 -- Function File: A = gallery ("randsvd", N, KAPPA, MODE, KL)
 -- Function File: A = gallery ("randsvd", N, KAPPA, MODE, KL, KU)
     Create a random matrix with pre-assigned singular values.

 -- Function File: A = gallery ("redheff", N)
     Create a zero and ones matrix of Redheffer associated with the
     Riemann hypothesis.

 -- Function File: A = gallery ("riemann", N)
     Create a matrix associated with the Riemann hypothesis.

 -- Function File: A = gallery ("ris", N)
     Create a symmetric Hankel matrix.

 -- Function File: A = gallery ("smoke", N)
 -- Function File: A = gallery ("smoke", N, K)
     Create a complex matrix, with a 'smoke ring' pseudospectrum.

 -- Function File: T = gallery ("toeppd", N)
 -- Function File: T = gallery ("toeppd", N, M)
 -- Function File: T = gallery ("toeppd", N, M, W)
 -- Function File: T = gallery ("toeppd", N, M, W, THETA)
     Create a symmetric positive definite Toeplitz matrix.

 -- Function File: P = gallery ("toeppen", N)
 -- Function File: P = gallery ("toeppen", N, A)
 -- Function File: P = gallery ("toeppen", N, A, B)
 -- Function File: P = gallery ("toeppen", N, A, B, C)
 -- Function File: P = gallery ("toeppen", N, A, B, C, D)
 -- Function File: P = gallery ("toeppen", N, A, B, C, D, E)
     Create a pentadiagonal Toeplitz matrix (sparse).

 -- Function File: A = gallery ("tridiag", X, Y, Z)
 -- Function File: A = gallery ("tridiag", N)
 -- Function File: A = gallery ("tridiag", N, C, D, E)
     Create a tridiagonal matrix (sparse).

 -- Function File: T = gallery ("triw", N)
 -- Function File: T = gallery ("triw", N, ALPHA)
 -- Function File: T = gallery ("triw", N, ALPHA, K)
     Create an upper triangular matrix discussed by Kahan, Golub, and
     Wilkinson.

 -- Function File: A = gallery ("uniformdata", [M N ...], J)
 -- Function File: A = gallery ("uniformdata", M, N, ..., J)
 -- Function File: A = gallery ("uniformdata", ..., "CLASS")
     Create a matrix with random samples from the standard uniform
     distribution (range [0,1]).

     The first input is a matrix of dimensions describing the size of
     the output.  The dimensions can also be input as comma-separated
     arguments.

     The input J is an integer index in the range [0, 2^32-1].  The
     values of the output matrix are always exactly the same
     (reproducibility) for a given size input and J index.

     The final optional argument determines the class of the resulting
     matrix.  Possible values for CLASS: "single", "double".  The
     default is "double".

 -- Function File: A = gallery ("wathen", NX, NY)
 -- Function File: A = gallery ("wathen", NX, NY, K)
     Create the Wathen matrix.

 -- Function File: [A, B] = gallery ("wilk", N)
     Create various specific matrices devised/discussed by Wilkinson.

 -- Function File: hadamard (N)
     Construct a Hadamard matrix (Hn) of size N-by-N.  The size N must
     be of the form 2^k * p in which p is one of 1, 12, 20 or 28.  The
     returned matrix is normalized, meaning 'Hn(:,1) == 1' and
     'Hn(1,:) == 1'.

     Some of the properties of Hadamard matrices are:

        * 'kron (Hm, Hn)' is a Hadamard matrix of size M-by-N.

        * 'Hn * Hn' = N * eye (N)'.

        * The rows of Hn are orthogonal.

        * 'det (A) <= abs (det (Hn))' for all A with
          'abs (A(i, j)) <= 1'.

        * Multiplying any row or column by -1 and the matrix will remain
          a Hadamard matrix.

     See also: *note compan: XREFcompan, *note hankel: XREFhankel, *note
     toeplitz: XREFtoeplitz.

 -- Function File: hankel (C)
 -- Function File: hankel (C, R)
     Return the Hankel matrix constructed from the first column C, and
     (optionally) the last row R.  If the last element of C is not the
     same as the first element of R, the last element of C is used.  If
     the second argument is omitted, it is assumed to be a vector of
     zeros with the same size as C.

     A Hankel matrix formed from an m-vector C, and an n-vector R, has
     the elements

          H(i,j) = c(i+j-1),  i+j-1 <= m;
          H(i,j) = r(i+j-m),  otherwise

     See also: *note hadamard: XREFhadamard, *note toeplitz:
     XREFtoeplitz.

 -- Function File: hilb (N)
     Return the Hilbert matrix of order N.  The i,j element of a Hilbert
     matrix is defined as

          H(i, j) = 1 / (i + j - 1)

     Hilbert matrices are close to being singular which make them
     difficult to invert with numerical routines.  Comparing the
     condition number of a random matrix 5x5 matrix with that of a
     Hilbert matrix of order 5 reveals just how difficult the problem
     is.

          cond (rand (5))
             => 14.392
          cond (hilb (5))
             => 4.7661e+05

     See also: *note invhilb: XREFinvhilb.

 -- Function File: invhilb (N)
     Return the inverse of the Hilbert matrix of order N.  This can be
     computed exactly using


                      (i+j)         /n+i-1\  /n+j-1\   /i+j-2\ 2
           A(i,j) = -1      (i+j-1)(       )(       ) (       )
                                    \ n-j /  \ n-i /   \ i-2 /

                  = p(i) p(j) / (i+j-1)


     where

                       k  /k+n-1\   /n\
              p(k) = -1  (       ) (   )
                          \ k-1 /   \k/

     The validity of this formula can easily be checked by expanding the
     binomial coefficients in both formulas as factorials.  It can be
     derived more directly via the theory of Cauchy matrices.  See J. W.
     Demmel, 'Applied Numerical Linear Algebra', p.  92.

     Compare this with the numerical calculation of 'inverse (hilb
     (n))', which suffers from the ill-conditioning of the Hilbert
     matrix, and the finite precision of your computer's floating point
     arithmetic.

     See also: *note hilb: XREFhilb.

 -- Function File: magic (N)

     Create an N-by-N magic square.  A magic square is an arrangement of
     the integers '1:n^2' such that the row sums, column sums, and
     diagonal sums are all equal to the same value.

     Note: N must be greater than 2 for the magic square to exist.

 -- Function File: pascal (N)
 -- Function File: pascal (N, T)
     Return the Pascal matrix of order N if 'T = 0'.  T defaults to 0.
     Return the pseudo-lower triangular Cholesky factor of the Pascal
     matrix if 'T = 1' (The sign of some columns may be negative).  This
     matrix is its own inverse, that is 'pascal (N, 1) ^ 2 == eye (N)'.
     If 'T = -1', return the true Cholesky factor with strictly positive
     values on the diagonal.  If 'T = 2', return a transposed and
     permuted version of 'pascal (N, 1)', which is the cube root of the
     identity matrix.  That is, 'pascal (N, 2) ^ 3 == eye (N)'.

     See also: *note chol: XREFchol.

 -- Function File: rosser ()
     Return the Rosser matrix.  This is a difficult test case used to
     evaluate eigenvalue algorithms.

     See also: *note wilkinson: XREFwilkinson, *note eig: XREFeig.

 -- Function File: toeplitz (C)
 -- Function File: toeplitz (C, R)
     Return the Toeplitz matrix constructed from the first column C, and
     (optionally) the first row R.  If the first element of R is not the
     same as the first element of C, the first element of C is used.  If
     the second argument is omitted, the first row is taken to be the
     same as the first column.

     A square Toeplitz matrix has the form:

          c(0)  r(1)   r(2)  ...  r(n)
          c(1)  c(0)   r(1)  ... r(n-1)
          c(2)  c(1)   c(0)  ... r(n-2)
           .     .      .   .      .
           .     .      .     .    .
           .     .      .       .  .
          c(n) c(n-1) c(n-2) ...  c(0)

     See also: *note hankel: XREFhankel.

 -- Function File: vander (C)
 -- Function File: vander (C, N)
     Return the Vandermonde matrix whose next to last column is C.  If N
     is specified, it determines the number of columns; otherwise, N is
     taken to be equal to the length of C.

     A Vandermonde matrix has the form:

          c(1)^(n-1) ... c(1)^2  c(1)  1
          c(2)^(n-1) ... c(2)^2  c(2)  1
              .     .      .      .    .
              .       .    .      .    .
              .         .  .      .    .
          c(n)^(n-1) ... c(n)^2  c(n)  1

     See also: *note polyfit: XREFpolyfit.

 -- Function File: wilkinson (N)
     Return the Wilkinson matrix of order N.  Wilkinson matrices are
     symmetric and tridiagonal with pairs of nearly, but not exactly,
     equal eigenvalues.  They are useful in testing the behavior and
     performance of eigenvalue solvers.

     See also: *note rosser: XREFrosser, *note eig: XREFeig.


File: octave.info,  Node: Arithmetic,  Next: Linear Algebra,  Prev: Matrix Manipulation,  Up: Top

17 Arithmetic
*************

Unless otherwise noted, all of the functions described in this chapter
will work for real and complex scalar, vector, or matrix arguments.
Functions described as "mapping functions" apply the given operation
individually to each element when given a matrix argument.  For example:

     sin ([1, 2; 3, 4])
          =>  0.84147   0.90930
              0.14112  -0.75680

* Menu:

* Exponents and Logarithms::
* Complex Arithmetic::
* Trigonometry::
* Sums and Products::
* Utility Functions::
* Special Functions::
* Rational Approximations::
* Coordinate Transformations::
* Mathematical Constants::


File: octave.info,  Node: Exponents and Logarithms,  Next: Complex Arithmetic,  Up: Arithmetic

17.1 Exponents and Logarithms
=============================

 -- Mapping Function: exp (X)
     Compute 'e^x' for each element of X.  To compute the matrix
     exponential, see *note Linear Algebra::.

     See also: *note log: XREFlog.

 -- Mapping Function: expm1 (X)
     Compute 'exp (X) - 1' accurately in the neighborhood of zero.

     See also: *note exp: XREFexp.

 -- Mapping Function: log (X)
     Compute the natural logarithm, 'ln (X)', for each element of X.  To
     compute the matrix logarithm, see *note Linear Algebra::.

     See also: *note exp: XREFexp, *note log1p: XREFlog1p, *note log2:
     XREFlog2, *note log10: XREFlog10, *note logspace: XREFlogspace.

 -- Function File: reallog (X)
     Return the real-valued natural logarithm of each element of X.

     If any element results in a complex return value 'reallog' aborts
     and issues an error.

     See also: *note log: XREFlog, *note realpow: XREFrealpow, *note
     realsqrt: XREFrealsqrt.

 -- Mapping Function: log1p (X)
     Compute 'log (1 + X)' accurately in the neighborhood of zero.

     See also: *note log: XREFlog, *note exp: XREFexp, *note expm1:
     XREFexpm1.

 -- Mapping Function: log10 (X)
     Compute the base-10 logarithm of each element of X.

     See also: *note log: XREFlog, *note log2: XREFlog2, *note logspace:
     XREFlogspace, *note exp: XREFexp.

 -- Mapping Function: log2 (X)
 -- Mapping Function: [F, E] = log2 (X)
     Compute the base-2 logarithm of each element of X.

     If called with two output arguments, split X into binary mantissa
     and exponent so that '1/2 <= abs(f) < 1' and E is an integer.  If
     'x = 0', 'f = e = 0'.

     See also: *note pow2: XREFpow2, *note log: XREFlog, *note log10:
     XREFlog10, *note exp: XREFexp.

 -- Function File: pow2 (X)
 -- Function File: pow2 (F, E)
     With one input argument, compute 2 .^ x for each element of X.

     With two input arguments, return f .* (2 .^ e).

     See also: *note log2: XREFlog2, *note nextpow2: XREFnextpow2, *note
     power: XREFpower.

 -- Function File: nextpow2 (X)
     Compute exponent for smallest power of two larger than input.

     For each element in the input array X, returns the first integer N
     such that 2^n >= abs (x).

     See also: *note pow2: XREFpow2, *note log2: XREFlog2.

 -- Function File: realpow (X, Y)
     Compute the real-valued, element-by-element power operator.

     This is equivalent to 'X .^ Y', except that 'realpow' reports an
     error if any return value is complex.

     See also: *note power: XREFpower, *note reallog: XREFreallog, *note
     realsqrt: XREFrealsqrt.

 -- Mapping Function: sqrt (X)
     Compute the square root of each element of X.  If X is negative, a
     complex result is returned.  To compute the matrix square root, see
     *note Linear Algebra::.

     See also: *note realsqrt: XREFrealsqrt, *note nthroot: XREFnthroot.

 -- Function File: realsqrt (X)
     Return the real-valued square root of each element of X.

     If any element results in a complex return value 'realsqrt' aborts
     and issues an error.

     See also: *note sqrt: XREFsqrt, *note realpow: XREFrealpow, *note
     reallog: XREFreallog.

 -- Mapping Function: cbrt (X)
     Compute the real cube root of each element of X.  Unlike 'X^(1/3)',
     the result will be negative if X is negative.

     See also: *note nthroot: XREFnthroot.

 -- Function File: nthroot (X, N)

     Compute the real (non-complex) N-th root of X.

     X must have all real entries and N must be a scalar.  If N is an
     even integer and X has negative entries then 'nthroot' aborts and
     issues an error.

     Example:

          nthroot (-1, 3)
          => -1
          (-1) ^ (1 / 3)
          => 0.50000 - 0.86603i

     See also: *note realsqrt: XREFrealsqrt, *note sqrt: XREFsqrt, *note
     cbrt: XREFcbrt.


File: octave.info,  Node: Complex Arithmetic,  Next: Trigonometry,  Prev: Exponents and Logarithms,  Up: Arithmetic

17.2 Complex Arithmetic
=======================

In the descriptions of the following functions, Z is the complex number
X + IY, where I is defined as 'sqrt (-1)'.

 -- Mapping Function: abs (Z)
     Compute the magnitude of Z, defined as |Z| = 'sqrt (x^2 + y^2)'.

     For example:

          abs (3 + 4i)
               => 5

 -- Mapping Function: arg (Z)
 -- Mapping Function: angle (Z)
     Compute the argument of Z, defined as, THETA = 'atan2 (Y, X)', in
     radians.

     For example:

          arg (3 + 4i)
               => 0.92730

 -- Mapping Function: conj (Z)
     Return the complex conjugate of Z, defined as 'conj (Z)' = X - IY.

     See also: *note real: XREFreal, *note imag: XREFimag.

 -- Function File: cplxpair (Z)
 -- Function File: cplxpair (Z, TOL)
 -- Function File: cplxpair (Z, TOL, DIM)
     Sort the numbers Z into complex conjugate pairs ordered by
     increasing real part.  Place the negative imaginary complex number
     first within each pair.  Place all the real numbers (those with
     'abs (imag (Z) / Z) < TOL') after the complex pairs.

     If TOL is unspecified the default value is 100*'eps'.

     By default the complex pairs are sorted along the first
     non-singleton dimension of Z.  If DIM is specified, then the
     complex pairs are sorted along this dimension.

     Signal an error if some complex numbers could not be paired.
     Signal an error if all complex numbers are not exact conjugates (to
     within TOL).  Note that there is no defined order for pairs with
     identical real parts but differing imaginary parts.

          cplxpair (exp(2i*pi*[0:4]'/5)) == exp(2i*pi*[3; 2; 4; 1; 0]/5)

 -- Mapping Function: imag (Z)
     Return the imaginary part of Z as a real number.

     See also: *note real: XREFreal, *note conj: XREFconj.

 -- Mapping Function: real (Z)
     Return the real part of Z.

     See also: *note imag: XREFimag, *note conj: XREFconj.


File: octave.info,  Node: Trigonometry,  Next: Sums and Products,  Prev: Complex Arithmetic,  Up: Arithmetic

17.3 Trigonometry
=================

Octave provides the following trigonometric functions where angles are
specified in radians.  To convert from degrees to radians multiply by
'pi/180' (e.g., 'sin (30 * pi/180)' returns the sine of 30 degrees).  As
an alternative, Octave provides a number of trigonometric functions
which work directly on an argument specified in degrees.  These
functions are named after the base trigonometric function with a 'd'
suffix.  For example, 'sin' expects an angle in radians while 'sind'
expects an angle in degrees.

   Octave uses the C library trigonometric functions.  It is expected
that these functions are defined by the ISO/IEC 9899 Standard.  This
Standard is available at:
<http://www.open-std.org/jtc1/sc22/wg14/www/docs/n1124.pdf>.  Section
F.9.1 deals with the trigonometric functions.  The behavior of most of
the functions is relatively straightforward.  However, there are some
exceptions to the standard behavior.  Many of the exceptions involve the
behavior for -0.  The most complex case is atan2.  Octave exactly
implements the behavior given in the Standard.  Including 'atan2(+- 0,
0)' returns '+- pi'.

   It should be noted that MATLAB uses different definitions which
apparently do not distinguish -0.

 -- Mapping Function: sin (X)
     Compute the sine for each element of X in radians.

     See also: *note asin: XREFasin, *note sind: XREFsind, *note sinh:
     XREFsinh.

 -- Mapping Function: cos (X)
     Compute the cosine for each element of X in radians.

     See also: *note acos: XREFacos, *note cosd: XREFcosd, *note cosh:
     XREFcosh.

 -- Mapping Function: tan (Z)
     Compute the tangent for each element of X in radians.

     See also: *note atan: XREFatan, *note tand: XREFtand, *note tanh:
     XREFtanh.

 -- Mapping Function: sec (X)
     Compute the secant for each element of X in radians.

     See also: *note asec: XREFasec, *note secd: XREFsecd, *note sech:
     XREFsech.

 -- Mapping Function: csc (X)
     Compute the cosecant for each element of X in radians.

     See also: *note acsc: XREFacsc, *note cscd: XREFcscd, *note csch:
     XREFcsch.

 -- Mapping Function: cot (X)
     Compute the cotangent for each element of X in radians.

     See also: *note acot: XREFacot, *note cotd: XREFcotd, *note coth:
     XREFcoth.

 -- Mapping Function: asin (X)
     Compute the inverse sine in radians for each element of X.

     See also: *note sin: XREFsin, *note asind: XREFasind.

 -- Mapping Function: acos (X)
     Compute the inverse cosine in radians for each element of X.

     See also: *note cos: XREFcos, *note acosd: XREFacosd.

 -- Mapping Function: atan (X)
     Compute the inverse tangent in radians for each element of X.

     See also: *note tan: XREFtan, *note atand: XREFatand.

 -- Mapping Function: asec (X)
     Compute the inverse secant in radians for each element of X.

     See also: *note sec: XREFsec, *note asecd: XREFasecd.

 -- Mapping Function: acsc (X)
     Compute the inverse cosecant in radians for each element of X.

     See also: *note csc: XREFcsc, *note acscd: XREFacscd.

 -- Mapping Function: acot (X)
     Compute the inverse cotangent in radians for each element of X.

     See also: *note cot: XREFcot, *note acotd: XREFacotd.

 -- Mapping Function: sinh (X)
     Compute the hyperbolic sine for each element of X.

     See also: *note asinh: XREFasinh, *note cosh: XREFcosh, *note tanh:
     XREFtanh.

 -- Mapping Function: cosh (X)
     Compute the hyperbolic cosine for each element of X.

     See also: *note acosh: XREFacosh, *note sinh: XREFsinh, *note tanh:
     XREFtanh.

 -- Mapping Function: tanh (X)
     Compute hyperbolic tangent for each element of X.

     See also: *note atanh: XREFatanh, *note sinh: XREFsinh, *note cosh:
     XREFcosh.

 -- Mapping Function: sech (X)
     Compute the hyperbolic secant of each element of X.

     See also: *note asech: XREFasech.

 -- Mapping Function: csch (X)
     Compute the hyperbolic cosecant of each element of X.

     See also: *note acsch: XREFacsch.

 -- Mapping Function: coth (X)
     Compute the hyperbolic cotangent of each element of X.

     See also: *note acoth: XREFacoth.

 -- Mapping Function: asinh (X)
     Compute the inverse hyperbolic sine for each element of X.

     See also: *note sinh: XREFsinh.

 -- Mapping Function: acosh (X)
     Compute the inverse hyperbolic cosine for each element of X.

     See also: *note cosh: XREFcosh.

 -- Mapping Function: atanh (X)
     Compute the inverse hyperbolic tangent for each element of X.

     See also: *note tanh: XREFtanh.

 -- Mapping Function: asech (X)
     Compute the inverse hyperbolic secant of each element of X.

     See also: *note sech: XREFsech.

 -- Mapping Function: acsch (X)
     Compute the inverse hyperbolic cosecant of each element of X.

     See also: *note csch: XREFcsch.

 -- Mapping Function: acoth (X)
     Compute the inverse hyperbolic cotangent of each element of X.

     See also: *note coth: XREFcoth.

 -- Mapping Function: atan2 (Y, X)
     Compute atan (Y / X) for corresponding elements of Y and X.  Signal
     an error if Y and X do not match in size and orientation.

     See also: *note tan: XREFtan, *note tand: XREFtand, *note tanh:
     XREFtanh, *note atanh: XREFatanh.

   Octave provides the following trigonometric functions where angles
are specified in degrees.  These functions produce true zeros at the
appropriate intervals rather than the small round-off error that occurs
when using radians.  For example:

     cosd (90)
          => 0
     cos (pi/2)
          => 6.1230e-17

 -- Function File: sind (X)
     Compute the sine for each element of X in degrees.  Returns zero
     for elements where 'X/180' is an integer.

     See also: *note asind: XREFasind, *note sin: XREFsin.

 -- Function File: cosd (X)
     Compute the cosine for each element of X in degrees.  Returns zero
     for elements where '(X-90)/180' is an integer.

     See also: *note acosd: XREFacosd, *note cos: XREFcos.

 -- Function File: tand (X)
     Compute the tangent for each element of X in degrees.  Returns zero
     for elements where 'X/180' is an integer and 'Inf' for elements
     where '(X-90)/180' is an integer.

     See also: *note atand: XREFatand, *note tan: XREFtan.

 -- Function File: secd (X)
     Compute the secant for each element of X in degrees.

     See also: *note asecd: XREFasecd, *note sec: XREFsec.

 -- Function File: cscd (X)
     Compute the cosecant for each element of X in degrees.

     See also: *note acscd: XREFacscd, *note csc: XREFcsc.

 -- Function File: cotd (X)
     Compute the cotangent for each element of X in degrees.

     See also: *note acotd: XREFacotd, *note cot: XREFcot.

 -- Function File: asind (X)
     Compute the inverse sine in degrees for each element of X.

     See also: *note sind: XREFsind, *note asin: XREFasin.

 -- Function File: acosd (X)
     Compute the inverse cosine in degrees for each element of X.

     See also: *note cosd: XREFcosd, *note acos: XREFacos.

 -- Function File: atand (X)
     Compute the inverse tangent in degrees for each element of X.

     See also: *note tand: XREFtand, *note atan: XREFatan.

 -- Function File: atan2d (Y, X)
     Compute atan2 (Y / X) in degrees for corresponding elements from Y
     and X.

     See also: *note tand: XREFtand, *note atan2: XREFatan2.

 -- Function File: asecd (X)
     Compute the inverse secant in degrees for each element of X.

     See also: *note secd: XREFsecd, *note asec: XREFasec.

 -- Function File: acscd (X)
     Compute the inverse cosecant in degrees for each element of X.

     See also: *note cscd: XREFcscd, *note acsc: XREFacsc.

 -- Function File: acotd (X)
     Compute the inverse cotangent in degrees for each element of X.

     See also: *note cotd: XREFcotd, *note acot: XREFacot.


File: octave.info,  Node: Sums and Products,  Next: Utility Functions,  Prev: Trigonometry,  Up: Arithmetic

17.4 Sums and Products
======================

 -- Built-in Function: sum (X)
 -- Built-in Function: sum (X, DIM)
 -- Built-in Function: sum (..., "native")
 -- Built-in Function: sum (..., "double")
 -- Built-in Function: sum (..., "extra")
     Sum of elements along dimension DIM.

     If DIM is omitted, it defaults to the first non-singleton
     dimension.

     The optional "type" input determines the class of the variable used
     for calculations.  If the argument "native" is given, then the
     operation is performed in the same type as the original argument,
     rather than the default double type.

     For example:

          sum ([true, true])
             => 2
          sum ([true, true], "native")
             => true

     On the contrary, if "double" is given, the sum is performed in
     double precision even for single precision inputs.

     For double precision inputs, the "extra" option will use a more
     accurate algorithm than straightforward summation.  For single
     precision inputs, "extra" is the same as "double".  Otherwise,
     "extra" has no effect.

     See also: *note cumsum: XREFcumsum, *note sumsq: XREFsumsq, *note
     prod: XREFprod.

 -- Built-in Function: prod (X)
 -- Built-in Function: prod (X, DIM)
 -- Built-in Function: prod (..., "native")
 -- Built-in Function: prod (..., "double")
     Product of elements along dimension DIM.

     If DIM is omitted, it defaults to the first non-singleton
     dimension.

     The optional "type" input determines the class of the variable used
     for calculations.  If the argument "native" is given, then the
     operation is performed in the same type as the original argument,
     rather than the default double type.

     For example:

          prod ([true, true])
             => 1
          prod ([true, true], "native")
             => true

     On the contrary, if "double" is given, the operation is performed
     in double precision even for single precision inputs.

     See also: *note cumprod: XREFcumprod, *note sum: XREFsum.

 -- Built-in Function: cumsum (X)
 -- Built-in Function: cumsum (X, DIM)
 -- Built-in Function: cumsum (..., "native")
 -- Built-in Function: cumsum (..., "double")
 -- Built-in Function: cumsum (..., "extra")
     Cumulative sum of elements along dimension DIM.  If DIM is omitted,
     it defaults to the first non-singleton dimension.

     See 'sum' for an explanation of the optional parameters "native",
     "double", and "extra".

     See also: *note sum: XREFsum, *note cumprod: XREFcumprod.

 -- Built-in Function: cumprod (X)
 -- Built-in Function: cumprod (X, DIM)
     Cumulative product of elements along dimension DIM.  If DIM is
     omitted, it defaults to the first non-singleton dimension.

     See also: *note prod: XREFprod, *note cumsum: XREFcumsum.

 -- Built-in Function: sumsq (X)
 -- Built-in Function: sumsq (X, DIM)
     Sum of squares of elements along dimension DIM.  If DIM is omitted,
     it defaults to the first non-singleton dimension.

     This function is conceptually equivalent to computing

          sum (x .* conj (x), dim)

     but it uses less memory and avoids calling 'conj' if X is real.

     See also: *note sum: XREFsum, *note prod: XREFprod.


File: octave.info,  Node: Utility Functions,  Next: Special Functions,  Prev: Sums and Products,  Up: Arithmetic

17.5 Utility Functions
======================

 -- Mapping Function: ceil (X)
     Return the smallest integer not less than X.  This is equivalent to
     rounding towards positive infinity.  If X is complex, return 'ceil
     (real (X)) + ceil (imag (X)) * I'.

          ceil ([-2.7, 2.7])
              => -2    3

     See also: *note floor: XREFfloor, *note round: XREFround, *note
     fix: XREFfix.

 -- Mapping Function: fix (X)
     Truncate fractional portion of X and return the integer portion.
     This is equivalent to rounding towards zero.  If X is complex,
     return 'fix (real (X)) + fix (imag (X)) * I'.

          fix ([-2.7, 2.7])
             => -2    2

     See also: *note ceil: XREFceil, *note floor: XREFfloor, *note
     round: XREFround.

 -- Mapping Function: floor (X)
     Return the largest integer not greater than X.  This is equivalent
     to rounding towards negative infinity.  If X is complex, return
     'floor (real (X)) + floor (imag (X)) * I'.

          floor ([-2.7, 2.7])
               => -3    2

     See also: *note ceil: XREFceil, *note round: XREFround, *note fix:
     XREFfix.

 -- Mapping Function: round (X)
     Return the integer nearest to X.  If X is complex, return 'round
     (real (X)) + round (imag (X)) * I'.  If there are two nearest
     integers, return the one further away from zero.

          round ([-2.7, 2.7])
               => -3    3

     See also: *note ceil: XREFceil, *note floor: XREFfloor, *note fix:
     XREFfix, *note roundb: XREFroundb.

 -- Mapping Function: roundb (X)
     Return the integer nearest to X.  If there are two nearest
     integers, return the even one (banker's rounding).  If X is
     complex, return 'roundb (real (X)) + roundb (imag (X)) * I'.

     See also: *note round: XREFround.

 -- Built-in Function: max (X)
 -- Built-in Function: max (X, [], DIM)
 -- Built-in Function: [W, IW] = max (X)
 -- Built-in Function: max (X, Y)
     Find maximum values in the array X.

     For a vector argument, return the maximum value.  For a matrix
     argument, return a row vector with the maximum value of each
     column.  For a multi-dimensional array, 'max' operates along the
     first non-singleton dimension.

     If the optional third argument DIM is present then operate along
     this dimension.  In this case the second argument is ignored and
     should be set to the empty matrix.

     For two matrices (or a matrix and a scalar), return the pairwise
     maximum.

     Thus,

          max (max (X))

     returns the largest element of the 2-D matrix X, and

          max (2:5, pi)
              =>  3.1416  3.1416  4.0000  5.0000

     compares each element of the range '2:5' with 'pi', and returns a
     row vector of the maximum values.

     For complex arguments, the magnitude of the elements are used for
     comparison.  If the magnitudes are identical, then the results are
     ordered by phase angle in the range (-pi, pi].  Hence,

          max ([-1 i 1 -i])
              => -1

     because all entries have magnitude 1, but -1 has the largest phase
     angle with value pi.

     If called with one input and two output arguments, 'max' also
     returns the first index of the maximum value(s).  Thus,

          [x, ix] = max ([1, 3, 5, 2, 5])
              =>  x = 5
                  ix = 3

     See also: *note min: XREFmin, *note cummax: XREFcummax, *note
     cummin: XREFcummin.

 -- Built-in Function: min (X)
 -- Built-in Function: min (X, [], DIM)
 -- Built-in Function: [W, IW] = min (X)
 -- Built-in Function: min (X, Y)
     Find minimum values in the array X.

     For a vector argument, return the minimum value.  For a matrix
     argument, return a row vector with the minimum value of each
     column.  For a multi-dimensional array, 'min' operates along the
     first non-singleton dimension.

     If the optional third argument DIM is present then operate along
     this dimension.  In this case the second argument is ignored and
     should be set to the empty matrix.

     For two matrices (or a matrix and a scalar), return the pairwise
     minimum.

     Thus,

          min (min (X))

     returns the smallest element of the 2-D matrix X, and

          min (2:5, pi)
              =>  2.0000  3.0000  3.1416  3.1416

     compares each element of the range '2:5' with 'pi', and returns a
     row vector of the minimum values.

     For complex arguments, the magnitude of the elements are used for
     comparison.  If the magnitudes are identical, then the results are
     ordered by phase angle in the range (-pi, pi].  Hence,

          min ([-1 i 1 -i])
              => -i

     because all entries have magnitude 1, but -i has the smallest phase
     angle with value -pi/2.

     If called with one input and two output arguments, 'min' also
     returns the first index of the minimum value(s).  Thus,

          [x, ix] = min ([1, 3, 0, 2, 0])
              =>  x = 0
                  ix = 3

     See also: *note max: XREFmax, *note cummin: XREFcummin, *note
     cummax: XREFcummax.

 -- Built-in Function: cummax (X)
 -- Built-in Function: cummax (X, DIM)
 -- Built-in Function: [W, IW] = cummax (...)
     Return the cumulative maximum values along dimension DIM.

     If DIM is unspecified it defaults to column-wise operation.  For
     example:

          cummax ([1 3 2 6 4 5])
             =>  1  3  3  6  6  6

     If called with two output arguments the index of the maximum value
     is also returned.

          [w, iw] = cummax ([1 3 2 6 4 5])
          =>
          w =  1  3  3  6  6  6
          iw = 1  2  2  4  4  4

     See also: *note cummin: XREFcummin, *note max: XREFmax, *note min:
     XREFmin.

 -- Built-in Function: cummin (X)
 -- Built-in Function: cummin (X, DIM)
 -- Built-in Function: [W, IW] = cummin (X)
     Return the cumulative minimum values along dimension DIM.

     If DIM is unspecified it defaults to column-wise operation.  For
     example:

          cummin ([5 4 6 2 3 1])
             =>  5  4  4  2  2  1

     If called with two output arguments the index of the minimum value
     is also returned.

          [w, iw] = cummin ([5 4 6 2 3 1])
          =>
          w =  5  4  4  2  2  1
          iw = 1  2  2  4  4  6

     See also: *note cummax: XREFcummax, *note min: XREFmin, *note max:
     XREFmax.

 -- Built-in Function: hypot (X, Y)
 -- Built-in Function: hypot (X, Y, Z, ...)
     Compute the element-by-element square root of the sum of the
     squares of X and Y.  This is equivalent to 'sqrt (X.^2 + Y.^2)',
     but calculated in a manner that avoids overflows for large values
     of X or Y.  'hypot' can also be called with more than 2 arguments;
     in this case, the arguments are accumulated from left to right:

          hypot (hypot (X, Y), Z)
          hypot (hypot (hypot (X, Y), Z), W), etc.

 -- Function File: DX = gradient (M)
 -- Function File: [DX, DY, DZ, ...] = gradient (M)
 -- Function File: [...] = gradient (M, S)
 -- Function File: [...] = gradient (M, X, Y, Z, ...)
 -- Function File: [...] = gradient (F, X0)
 -- Function File: [...] = gradient (F, X0, S)
 -- Function File: [...] = gradient (F, X0, X, Y, ...)

     Calculate the gradient of sampled data or a function.  If M is a
     vector, calculate the one-dimensional gradient of M.  If M is a
     matrix the gradient is calculated for each dimension.

     '[DX, DY] = gradient (M)' calculates the one dimensional gradient
     for X and Y direction if M is a matrix.  Additional return
     arguments can be use for multi-dimensional matrices.

     A constant spacing between two points can be provided by the S
     parameter.  If S is a scalar, it is assumed to be the spacing for
     all dimensions.  Otherwise, separate values of the spacing can be
     supplied by the X, ... arguments.  Scalar values specify an
     equidistant spacing.  Vector values for the X, ... arguments
     specify the coordinate for that dimension.  The length must match
     their respective dimension of M.

     At boundary points a linear extrapolation is applied.  Interior
     points are calculated with the first approximation of the numerical
     gradient

          y'(i) = 1/(x(i+1)-x(i-1)) * (y(i-1)-y(i+1)).

     If the first argument F is a function handle, the gradient of the
     function at the points in X0 is approximated using central
     difference.  For example, 'gradient (@cos, 0)' approximates the
     gradient of the cosine function in the point x0 = 0.  As with
     sampled data, the spacing values between the points from which the
     gradient is estimated can be set via the S or DX, DY, ...
     arguments.  By default a spacing of 1 is used.

     See also: *note diff: XREFdiff, *note del2: XREFdel2.

 -- Built-in Function: dot (X, Y, DIM)
     Compute the dot product of two vectors.  If X and Y are matrices,
     calculate the dot products along the first non-singleton dimension.
     If the optional argument DIM is given, calculate the dot products
     along this dimension.

     This is equivalent to 'sum (conj (X) .* Y, DIM)', but avoids
     forming a temporary array and is faster.  When X and Y are column
     vectors, the result is equivalent to 'X' * Y'.

     See also: *note cross: XREFcross, *note divergence: XREFdivergence.

 -- Function File: cross (X, Y)
 -- Function File: cross (X, Y, DIM)
     Compute the vector cross product of two 3-dimensional vectors X and
     Y.

          cross ([1,1,0], [0,1,1])
               => [ 1; -1; 1 ]

     If X and Y are matrices, the cross product is applied along the
     first dimension with 3 elements.  The optional argument DIM forces
     the cross product to be calculated along the specified dimension.

     See also: *note dot: XREFdot, *note curl: XREFcurl, *note
     divergence: XREFdivergence.

 -- Function File: DIV = divergence (X, Y, Z, FX, FY, FZ)
 -- Function File: DIV = divergence (FX, FY, FZ)
 -- Function File: DIV = divergence (X, Y, FX, FY)
 -- Function File: DIV = divergence (FX, FY)
     Calculate divergence of a vector field given by the arrays FX, FY,
     and FZ or FX, FY respectively.

                            d               d               d
          div F(x,y,z)  =   -- F(x,y,z)  +  -- F(x,y,z)  +  -- F(x,y,z)
                            dx              dy              dz

     The coordinates of the vector field can be given by the arguments
     X, Y, Z or X, Y respectively.

     See also: *note curl: XREFcurl, *note gradient: XREFgradient, *note
     del2: XREFdel2, *note dot: XREFdot.

 -- Function File: [CX, CY, CZ, V] = curl (X, Y, Z, FX, FY, FZ)
 -- Function File: [CZ, V] = curl (X, Y, FX, FY)
 -- Function File: [...] = curl (FX, FY, FZ)
 -- Function File: [...] = curl (FX, FY)
 -- Function File: V = curl (...)
     Calculate curl of vector field given by the arrays FX, FY, and FZ
     or FX, FY respectively.

                            / d         d       d         d       d         d     \
          curl F(x,y,z)  =  | -- Fz  -  -- Fy,  -- Fx  -  -- Fz,  -- Fy  -  -- Fx |
                            \ dy        dz      dz        dx      dx        dy    /

     The coordinates of the vector field can be given by the arguments
     X, Y, Z or X, Y respectively.  V calculates the scalar component of
     the angular velocity vector in direction of the z-axis for
     two-dimensional input.  For three-dimensional input the scalar
     rotation is calculated at each grid point in direction of the
     vector field at that point.

     See also: *note divergence: XREFdivergence, *note gradient:
     XREFgradient, *note del2: XREFdel2, *note cross: XREFcross.

 -- Function File: D = del2 (M)
 -- Function File: D = del2 (M, H)
 -- Function File: D = del2 (M, DX, DY, ...)

     Calculate the discrete Laplace operator.  For a 2-dimensional
     matrix M this is defined as

                1    / d^2            d^2         \
          D  = --- * | ---  M(x,y) +  ---  M(x,y) |
                4    \ dx^2           dy^2        /

     For N-dimensional arrays the sum in parentheses is expanded to
     include second derivatives over the additional higher dimensions.

     The spacing between evaluation points may be defined by H, which is
     a scalar defining the equidistant spacing in all dimensions.
     Alternatively, the spacing in each dimension may be defined
     separately by DX, DY, etc.  A scalar spacing argument defines
     equidistant spacing, whereas a vector argument can be used to
     specify variable spacing.  The length of the spacing vectors must
     match the respective dimension of M.  The default spacing value is
     1.

     At least 3 data points are needed for each dimension.  Boundary
     points are calculated from the linear extrapolation of interior
     points.

     See also: *note gradient: XREFgradient, *note diff: XREFdiff.

 -- Function File: factorial (N)
     Return the factorial of N where N is a real non-negative integer.

     If N is a scalar, this is equivalent to 'prod (1:N)'.  For vector
     or matrix arguments, return the factorial of each element in the
     array.

     For non-integers see the generalized factorial function 'gamma'.
     Note that the factorial function grows large quite quickly, and
     even with double precision values overflow will occur if N > 171.
     For such cases consider 'gammaln'.

     See also: *note prod: XREFprod, *note gamma: XREFgamma, *note
     gammaln: XREFgammaln.

 -- Function File: PF = factor (Q)
 -- Function File: [PF, N] = factor (Q)
     Return the prime factorization of Q.

     The prime factorization is defined as 'prod (PF) == Q' where every
     element of PF is a prime number.  If 'Q == 1', return 1.

     With two output arguments, return the unique prime factors PF and
     their multiplicities.  That is, 'prod (PF .^ N) == Q'.

     Implementation Note: The input Q must be less than 'bitmax'
     (9.0072e+15) in order to factor correctly.

     See also: *note gcd: XREFgcd, *note lcm: XREFlcm, *note isprime:
     XREFisprime, *note primes: XREFprimes.

 -- Built-in Function: G = gcd (A1, A2, ...)
 -- Built-in Function: [G, V1, ...] = gcd (A1, A2, ...)
     Compute the greatest common divisor of A1, A2, ....

     If more than one argument is given then all arguments must be the
     same size or scalar.  In this case the greatest common divisor is
     calculated for each element individually.  All elements must be
     ordinary or Gaussian (complex) integers.  Note that for Gaussian
     integers, the gcd is only unique up to a phase factor
     (multiplication by 1, -1, i, or -i), so an arbitrary greatest
     common divisor amongst four possible is returned.

     Optional return arguments V1, ..., contain integer vectors such
     that,

          G = V1 .* A1 + V2 .* A2 + ...

     Example code:

          gcd ([15, 9], [20, 18])
             =>  5  9

     See also: *note lcm: XREFlcm, *note factor: XREFfactor, *note
     isprime: XREFisprime.

 -- Mapping Function: lcm (X, Y)
 -- Mapping Function: lcm (X, Y, ...)
     Compute the least common multiple of X and Y, or of the list of all
     arguments.

     All elements must be numeric and of the same size or scalar.

     See also: *note factor: XREFfactor, *note gcd: XREFgcd, *note
     isprime: XREFisprime.

 -- Function File: chop (X, NDIGITS, BASE)
     Truncate elements of X to a length of NDIGITS such that the
     resulting numbers are exactly divisible by BASE.  If BASE is not
     specified it defaults to 10.

          chop (-pi, 5, 10)
             => -3.14200000000000
          chop (-pi, 5, 5)
             => -3.14150000000000

 -- Mapping Function: rem (X, Y)
     Return the remainder of the division 'X / Y', computed using the
     expression

          x - y .* fix (x ./ y)

     An error message is printed if the dimensions of the arguments do
     not agree, or if either of the arguments is complex.

     See also: *note mod: XREFmod.

 -- Mapping Function: mod (X, Y)
     Compute the modulo of X and Y.  Conceptually this is given by

          x - y .* floor (x ./ y)

     and is written such that the correct modulus is returned for
     integer types.  This function handles negative values correctly.
     That is, 'mod (-1, 3)' is 2, not -1, as 'rem (-1, 3)' returns.
     'mod (X, 0)' returns X.

     An error results if the dimensions of the arguments do not agree,
     or if either of the arguments is complex.

     See also: *note rem: XREFrem.

 -- Function File: primes (N)
     Return all primes up to N.

     The output data class (double, single, uint32, etc.)  is the same
     as the input class of N.  The algorithm used is the Sieve of
     Eratosthenes.

     Notes: If you need a specific number of primes you can use the fact
     that the distance from one prime to the next is, on average,
     proportional to the logarithm of the prime.  Integrating, one finds
     that there are about k primes less than k*log (5*k).

     See also 'list_primes' if you need a specific number N of primes.

     See also: *note list_primes: XREFlist_primes, *note isprime:
     XREFisprime.

 -- Function File: list_primes ()
 -- Function File: list_primes (N)
     List the first N primes.

     If N is unspecified, the first 25 primes are listed.

     See also: *note primes: XREFprimes, *note isprime: XREFisprime.

 -- Mapping Function: sign (X)
     Compute the "signum" function, which is defined as

                     -1, x < 0;
          sign (x) =  0, x = 0;
                      1, x > 0.

     For complex arguments, 'sign' returns 'x ./ abs (X)'.

     Note that 'sign (-0.0)' is 0.  Although IEEE 754 floating point
     allows zero to be signed, 0.0 and -0.0 compare equal.  If you must
     test whether zero is signed, use the 'signbit' function.

     See also: *note signbit: XREFsignbit.

 -- Mapping Function: signbit (X)
     Return logical true if the value of X has its sign bit set.
     Otherwise return logical false.  This behavior is consistent with
     the other logical functions.  See*note Logical Values::.  The
     behavior differs from the C language function which returns nonzero
     if the sign bit is set.

     This is not the same as 'x < 0.0', because IEEE 754 floating point
     allows zero to be signed.  The comparison '-0.0 < 0.0' is false,
     but 'signbit (-0.0)' will return a nonzero value.

     See also: *note sign: XREFsign.


File: octave.info,  Node: Special Functions,  Next: Rational Approximations,  Prev: Utility Functions,  Up: Arithmetic

17.6 Special Functions
======================

 -- Built-in Function: [A, IERR] = airy (K, Z, OPT)
     Compute Airy functions of the first and second kind, and their
     derivatives.

           K   Function   Scale factor (if "opt" is supplied)
          ---  --------   ---------------------------------------
           0   Ai (Z)     exp ((2/3) * Z * sqrt (Z))
           1   dAi(Z)/dZ  exp ((2/3) * Z * sqrt (Z))
           2   Bi (Z)     exp (-abs (real ((2/3) * Z * sqrt (Z))))
           3   dBi(Z)/dZ  exp (-abs (real ((2/3) * Z * sqrt (Z))))

     The function call 'airy (Z)' is equivalent to 'airy (0, Z)'.

     The result is the same size as Z.

     If requested, IERR contains the following status information and is
     the same size as the result.

       0. Normal return.

       1. Input error, return 'NaN'.

       2. Overflow, return 'Inf'.

       3. Loss of significance by argument reduction results in less
          than half of machine accuracy.

       4. Complete loss of significance by argument reduction, return
          'NaN'.

       5. Error--no computation, algorithm termination condition not
          met, return 'NaN'.

 -- Built-in Function: [J, IERR] = besselj (ALPHA, X, OPT)
 -- Built-in Function: [Y, IERR] = bessely (ALPHA, X, OPT)
 -- Built-in Function: [I, IERR] = besseli (ALPHA, X, OPT)
 -- Built-in Function: [K, IERR] = besselk (ALPHA, X, OPT)
 -- Built-in Function: [H, IERR] = besselh (ALPHA, K, X, OPT)
     Compute Bessel or Hankel functions of various kinds:

     'besselj'
          Bessel functions of the first kind.  If the argument OPT is
          supplied, the result is multiplied by 'exp (-abs (imag (X)))'.

     'bessely'
          Bessel functions of the second kind.  If the argument OPT is
          supplied, the result is multiplied by 'exp (-abs (imag (X)))'.

     'besseli'

          Modified Bessel functions of the first kind.  If the argument
          OPT is supplied, the result is multiplied by 'exp (-abs (real
          (X)))'.

     'besselk'

          Modified Bessel functions of the second kind.  If the argument
          OPT is supplied, the result is multiplied by 'exp (X)'.

     'besselh'
          Compute Hankel functions of the first (K = 1) or second (K =
          2) kind.  If the argument OPT is supplied, the result is
          multiplied by 'exp (-I*X)' for K = 1 or 'exp (I*X)' for K = 2.

     If ALPHA is a scalar, the result is the same size as X.  If X is a
     scalar, the result is the same size as ALPHA.  If ALPHA is a row
     vector and X is a column vector, the result is a matrix with
     'length (X)' rows and 'length (ALPHA)' columns.  Otherwise, ALPHA
     and X must conform and the result will be the same size.

     The value of ALPHA must be real.  The value of X may be complex.

     If requested, IERR contains the following status information and is
     the same size as the result.

       0. Normal return.

       1. Input error, return 'NaN'.

       2. Overflow, return 'Inf'.

       3. Loss of significance by argument reduction results in less
          than half of machine accuracy.

       4. Complete loss of significance by argument reduction, return
          'NaN'.

       5. Error--no computation, algorithm termination condition not
          met, return 'NaN'.

 -- Mapping Function: beta (A, B)
     Compute the Beta function for real inputs A and B.

     The Beta function definition is

          beta (a, b) = gamma (a) * gamma (b) / gamma (a + b).

     The Beta function can grow quite large and it is often more useful
     to work with the logarithm of the output rather than the function
     directly.  *Note betaln: XREFbetaln, for computing the logarithm of
     the Beta function in an efficient manner.

     See also: *note betaln: XREFbetaln, *note betainc: XREFbetainc,
     *note betaincinv: XREFbetaincinv.

 -- Mapping Function: betainc (X, A, B)
     Return the regularized incomplete Beta function,

                                             x
                                    1       /
          betainc (x, a, b) = -----------   | t^(a-1) (1-t)^(b-1) dt.
                              beta (a, b)   /
                                         t=0

     If X has more than one component, both A and B must be scalars.  If
     X is a scalar, A and B must be of compatible dimensions.

     See also: *note betaincinv: XREFbetaincinv, *note beta: XREFbeta,
     *note betaln: XREFbetaln.

 -- Mapping Function: betaincinv (Y, A, B)
     Compute the inverse of the incomplete Beta function, i.e., X such
     that

          Y == betainc (X, A, B)

     See also: *note betainc: XREFbetainc, *note beta: XREFbeta, *note
     betaln: XREFbetaln.

 -- Mapping Function: betaln (A, B)
     Compute the natural logarithm of the Beta function for real inputs
     A and B.

     'betaln' is defined as

          betaln (a, b) = log (beta (a, b))

     and is calculated in a way to reduce the occurrence of underflow.

     The Beta function can grow quite large and it is often more useful
     to work with the logarithm of the output rather than the function
     directly.

     See also: *note beta: XREFbeta, *note betainc: XREFbetainc, *note
     betaincinv: XREFbetaincinv, *note gammaln: XREFgammaln.

 -- Mapping Function: bincoeff (N, K)
     Return the binomial coefficient of N and K, defined as

           /   \
           | n |    n (n-1) (n-2) ... (n-k+1)
           |   |  = -------------------------
           | k |               k!
           \   /

     For example:

          bincoeff (5, 2)
             => 10

     In most cases, the 'nchoosek' function is faster for small scalar
     integer arguments.  It also warns about loss of precision for big
     arguments.

     See also: *note nchoosek: XREFnchoosek.

 -- Function File: commutation_matrix (M, N)
     Return the commutation matrix K(m,n) which is the unique M*N by M*N
     matrix such that K(m,n) * vec(A) = vec(A') for all m by n matrices
     A.

     If only one argument M is given, K(m,m) is returned.

     See Magnus and Neudecker (1988), 'Matrix Differential Calculus with
     Applications in Statistics and Econometrics.'

 -- Function File: duplication_matrix (N)
     Return the duplication matrix Dn which is the unique n^2 by
     n*(n+1)/2 matrix such that Dn vech (A) = vec (A) for all symmetric
     n by n matrices A.

     See Magnus and Neudecker (1988), 'Matrix Differential Calculus with
     Applications in Statistics and Econometrics.'

 -- Mapping Function: dawson (Z)
     Compute the Dawson (scaled imaginary error) function,

          (sqrt (pi) / 2) * exp (-z^2) * erfi (z)

     See also: *note erfc: XREFerfc, *note erf: XREFerf, *note erfcx:
     XREFerfcx, *note erfi: XREFerfi, *note erfinv: XREFerfinv, *note
     erfcinv: XREFerfcinv.

 -- Built-in Function: [SN, CN, DN, ERR] = ellipj (U, M)
 -- Built-in Function: [SN, CN, DN, ERR] = ellipj (U, M, TOL)
     Compute the Jacobi elliptic functions SN, CN, and DN of complex
     argument U and real parameter M.

     If M is a scalar, the results are the same size as U.  If U is a
     scalar, the results are the same size as M.  If U is a column
     vector and M is a row vector, the results are matrices with 'length
     (U)' rows and 'length (M)' columns.  Otherwise, U and M must
     conform in size and the results will be the same size as the
     inputs.

     The value of U may be complex.  The value of M must be 0 <= M <= 1.

     The optional input TOL is currently ignored (MATLAB uses this to
     allow faster, less accurate approximation).

     If requested, ERR contains the following status information and is
     the same size as the result.

       0. Normal return.

       1. Error--no computation, algorithm termination condition not
          met, return 'NaN'.

     Reference: Milton Abramowitz and Irene A Stegun, 'Handbook of
     Mathematical Functions', Chapter 16 (Sections 16.4, 16.13, and
     16.15), Dover, 1965.

     See also: *note ellipke: XREFellipke.

 -- Function File: K = ellipke (M)
 -- Function File: K = ellipke (M, TOL)
 -- Function File: [K, E] = ellipke (...)
     Compute complete elliptic integrals of the first K(M) and second
     E(M) kind.

     M must be a scalar or real array with -Inf <= M <= 1.

     The optional input TOL controls the stopping tolerance of the
     algorithm and defaults to 'eps (class (M))'.  The tolerance can be
     increased to compute a faster, less accurate approximation.

     When called with one output only elliptic integrals of the first
     kind are returned.

     Mathematical Note:

     Elliptic integrals of the first kind are defined as

                   1
                  /               dt
          K (m) = | ------------------------------
                  / sqrt ((1 - t^2)*(1 - m^2*t^2))
                 0

     Elliptic integrals of the second kind are defined as

                   1
                  /  sqrt (1 - m^2*t^2)
          E (m) = |  ------------------ dt
                  /  sqrt (1 - t^2)
                 0

     Reference: Milton Abramowitz and Irene A. Stegun, 'Handbook of
     Mathematical Functions', Chapter 17, Dover, 1965.

     See also: *note ellipj: XREFellipj.

 -- Mapping Function: erf (Z)
     Compute the error function,

                                  z
                        2        /
          erf (z) = --------- *  | e^(-t^2) dt
                    sqrt (pi)    /
                              t=0

     See also: *note erfc: XREFerfc, *note erfcx: XREFerfcx, *note erfi:
     XREFerfi, *note dawson: XREFdawson, *note erfinv: XREFerfinv, *note
     erfcinv: XREFerfcinv.

 -- Mapping Function: erfc (Z)
     Compute the complementary error function, '1 - erf (Z)'.

     See also: *note erfcinv: XREFerfcinv, *note erfcx: XREFerfcx, *note
     erfi: XREFerfi, *note dawson: XREFdawson, *note erf: XREFerf, *note
     erfinv: XREFerfinv.

 -- Mapping Function: erfcx (Z)
     Compute the scaled complementary error function,

          exp (z^2) * erfc (z)

     See also: *note erfc: XREFerfc, *note erf: XREFerf, *note erfi:
     XREFerfi, *note dawson: XREFdawson, *note erfinv: XREFerfinv, *note
     erfcinv: XREFerfcinv.

 -- Mapping Function: erfi (Z)
     Compute the imaginary error function,

          -i * erf (i*z)

     See also: *note erfc: XREFerfc, *note erf: XREFerf, *note erfcx:
     XREFerfcx, *note dawson: XREFdawson, *note erfinv: XREFerfinv,
     *note erfcinv: XREFerfcinv.

 -- Mapping Function: erfinv (X)
     Compute the inverse error function, i.e., Y such that

          erf (Y) == X

     See also: *note erf: XREFerf, *note erfc: XREFerfc, *note erfcx:
     XREFerfcx, *note erfi: XREFerfi, *note dawson: XREFdawson, *note
     erfcinv: XREFerfcinv.

 -- Mapping Function: erfcinv (X)
     Compute the inverse complementary error function, i.e., Y such that

          erfc (Y) == X

     See also: *note erfc: XREFerfc, *note erf: XREFerf, *note erfcx:
     XREFerfcx, *note erfi: XREFerfi, *note dawson: XREFdawson, *note
     erfinv: XREFerfinv.

 -- Function File: expint (X)
     Compute the exponential integral:

                     infinity
                    /
          E_1 (x) = | exp (-t)/t dt
                    /
                   x

     Note: For compatibility, this functions uses the MATLAB definition
     of the exponential integral.  Most other sources refer to this
     particular value as E_1 (x), and the exponential integral is

                      infinity
                     /
          Ei (x) = - | exp (-t)/t dt
                     /
                   -x

     The two definitions are related, for positive real values of X, by
     'E_1 (-x) = -Ei (x) - i*pi'.

 -- Mapping Function: gamma (Z)
     Compute the Gamma function,

                       infinity
                      /
          gamma (z) = | t^(z-1) exp (-t) dt.
                      /
                   t=0

     Programming Note: The gamma function can grow quite large even for
     small input values.  In many cases it may be preferable to use the
     natural logarithm of the gamma function ('gammaln') in calculations
     to minimize loss of precision.  The final result is then 'exp
     (RESULT_USING_GAMMALN).'

     See also: *note gammainc: XREFgammainc, *note gammaln: XREFgammaln,
     *note factorial: XREFfactorial.

 -- Mapping Function: gammainc (X, A)
 -- Mapping Function: gammainc (X, A, "lower")
 -- Mapping Function: gammainc (X, A, "upper")
     Compute the normalized incomplete gamma function,

                                          x
                                 1       /
          gammainc (x, a) = ---------    | exp (-t) t^(a-1) dt
                            gamma (a)    /
                                      t=0

     with the limiting value of 1 as X approaches infinity.  The
     standard notation is P(a,x), e.g., Abramowitz and Stegun (6.5.1).

     If A is scalar, then 'gammainc (X, A)' is returned for each element
     of X and vice versa.

     If neither X nor A is scalar, the sizes of X and A must agree, and
     'gammainc' is applied element-by-element.

     By default the incomplete gamma function integrated from 0 to X is
     computed.  If "upper" is given then the complementary function
     integrated from X to infinity is calculated.  It should be noted
     that

          gammainc (X, A) == 1 - gammainc (X, A, "upper")

     See also: *note gamma: XREFgamma, *note gammaln: XREFgammaln.

 -- Function File: L = legendre (N, X)
 -- Function File: L = legendre (N, X, NORMALIZATION)
     Compute the Legendre function of degree N and order M = 0 ... N.

     The value N must be a real non-negative integer.  X is a vector
     with real-valued elements in the range [-1, 1].

     The optional argument NORMALIZATION may be one of "unnorm", "sch",
     or "norm".  The default if no normalization is given is "unnorm".

     When the optional argument NORMALIZATION is "unnorm", compute the
     Legendre function of degree N and order M and return all values for
     M = 0 ... N.  The return value has one dimension more than X.

     The Legendre Function of degree N and order M:

           m         m      2  m/2   d^m
          P(x) = (-1) * (1-x  )    * ----  P(x)
           n                         dx^m   n

     with Legendre polynomial of degree N:

                    1    d^n   2    n
          P(x) = ------ [----(x - 1) ]
           n     2^n n!  dx^n

     'legendre (3, [-1.0, -0.9, -0.8])' returns the matrix:

           x  |   -1.0   |   -0.9   |   -0.8
          ------------------------------------
          m=0 | -1.00000 | -0.47250 | -0.08000
          m=1 |  0.00000 | -1.99420 | -1.98000
          m=2 |  0.00000 | -2.56500 | -4.32000
          m=3 |  0.00000 | -1.24229 | -3.24000

     When the optional argument 'normalization' is "sch", compute the
     Schmidt semi-normalized associated Legendre function.  The Schmidt
     semi-normalized associated Legendre function is related to the
     unnormalized Legendre functions by the following:

     For Legendre functions of degree N and order 0:

            0      0
          SP(x) = P(x)
            n      n

     For Legendre functions of degree n and order m:

            m      m         m    2(n-m)! 0.5
          SP(x) = P(x) * (-1)  * [-------]
            n      n              (n+m)!

     When the optional argument NORMALIZATION is "norm", compute the
     fully normalized associated Legendre function.  The fully
     normalized associated Legendre function is related to the
     unnormalized Legendre functions by the following:

     For Legendre functions of degree N and order M

            m      m         m    (n+0.5)(n-m)! 0.5
          NP(x) = P(x) * (-1)  * [-------------]
            n      n                  (n+m)!

 -- Mapping Function: gammaln (X)
 -- Mapping Function: lgamma (X)
     Return the natural logarithm of the gamma function of X.

     See also: *note gamma: XREFgamma, *note gammainc: XREFgammainc.


File: octave.info,  Node: Rational Approximations,  Next: Coordinate Transformations,  Prev: Special Functions,  Up: Arithmetic

17.7 Rational Approximations
============================

 -- Function File: S = rat (X, TOL)
 -- Function File: [N, D] = rat (X, TOL)

     Find a rational approximation to X within the tolerance defined by
     TOL using a continued fraction expansion.  For example:

          rat (pi) = 3 + 1/(7 + 1/16) = 355/113
          rat (e) = 3 + 1/(-4 + 1/(2 + 1/(5 + 1/(-2 + 1/(-7)))))
                  = 1457/536

     Called with two arguments returns the numerator and denominator
     separately as two matrices.

     See also: *note rats: XREFrats.

 -- Built-in Function: rats (X, LEN)
     Convert X into a rational approximation represented as a string.
     You can convert the string back into a matrix as follows:

          r = rats (hilb (4));
          x = str2num (r)

     The optional second argument defines the maximum length of the
     string representing the elements of X.  By default LEN is 9.

     If the length of the smallest possible rational approximation
     exceeds LEN, an asterisk (*) padded with spaces will be returned
     instead.

     See also: *note format: XREFformat, *note rat: XREFrat.


File: octave.info,  Node: Coordinate Transformations,  Next: Mathematical Constants,  Prev: Rational Approximations,  Up: Arithmetic

17.8 Coordinate Transformations
===============================

 -- Function File: [THETA, R] = cart2pol (X, Y)
 -- Function File: [THETA, R, Z] = cart2pol (X, Y, Z)
 -- Function File: [THETA, R] = cart2pol (C)
 -- Function File: [THETA, R, Z] = cart2pol (C)
 -- Function File: P = cart2pol (...)

     Transform Cartesian to polar or cylindrical coordinates.

     THETA describes the angle relative to the positive x-axis.  R is
     the distance to the z-axis (0, 0, z).  X, Y (, and Z) must be the
     same shape, or scalar.  If called with a single matrix argument
     then each row of C represents the Cartesian coordinate (X, Y (,
     Z)).

     If only a single return argument is requested then return a matrix
     P where each row represents one polar/(cylindrical) coordinate
     (THETA, PHI (, Z)).

     See also: *note pol2cart: XREFpol2cart, *note cart2sph:
     XREFcart2sph, *note sph2cart: XREFsph2cart.

 -- Function File: [X, Y] = pol2cart (THETA, R)
 -- Function File: [X, Y, Z] = pol2cart (THETA, R, Z)
 -- Function File: [X, Y] = pol2cart (P)
 -- Function File: [X, Y, Z] = pol2cart (P)
 -- Function File: C = pol2cart (...)
     Transform polar or cylindrical to Cartesian coordinates.

     THETA, R, (and Z) must be the same shape, or scalar.  THETA
     describes the angle relative to the positive x-axis.  R is the
     distance to the z-axis (0, 0, z).  If called with a single matrix
     argument then each row of P represents the polar/(cylindrical)
     coordinate (THETA, R (, Z)).

     If only a single return argument is requested then return a matrix
     C where each row represents one Cartesian coordinate (X, Y (, Z)).

     See also: *note cart2pol: XREFcart2pol, *note sph2cart:
     XREFsph2cart, *note cart2sph: XREFcart2sph.

 -- Function File: [THETA, PHI, R] = cart2sph (X, Y, Z)
 -- Function File: [THETA, PHI, R] = cart2sph (C)
 -- Function File: S = cart2sph (...)
     Transform Cartesian to spherical coordinates.

     THETA describes the angle relative to the positive x-axis.  PHI is
     the angle relative to the xy-plane.  R is the distance to the
     origin (0, 0, 0).  X, Y, and Z must be the same shape, or scalar.
     If called with a single matrix argument then each row of C
     represents the Cartesian coordinate (X, Y, Z).

     If only a single return argument is requested then return a matrix
     S where each row represents one spherical coordinate (THETA, PHI,
     R).

     See also: *note sph2cart: XREFsph2cart, *note cart2pol:
     XREFcart2pol, *note pol2cart: XREFpol2cart.

 -- Function File: [X, Y, Z] = sph2cart (THETA, PHI, R)
 -- Function File: [X, Y, Z] = sph2cart (S)
 -- Function File: C = sph2cart (...)
     Transform spherical to Cartesian coordinates.

     THETA describes the angle relative to the positive x-axis.  PHI is
     the angle relative to the xy-plane.  R is the distance to the
     origin (0, 0, 0).  THETA, PHI, and R must be the same shape, or
     scalar.  If called with a single matrix argument then each row of S
     represents the spherical coordinate (THETA, PHI, R).

     If only a single return argument is requested then return a matrix
     C where each row represents one Cartesian coordinate (X, Y, Z).

     See also: *note cart2sph: XREFcart2sph, *note pol2cart:
     XREFpol2cart, *note cart2pol: XREFcart2pol.


File: octave.info,  Node: Mathematical Constants,  Prev: Coordinate Transformations,  Up: Arithmetic

17.9 Mathematical Constants
===========================

 -- Built-in Function: e
 -- Built-in Function: e (N)
 -- Built-in Function: e (N, M)
 -- Built-in Function: e (N, M, K, ...)
 -- Built-in Function: e (..., CLASS)
     Return a scalar, matrix, or N-dimensional array whose elements are
     all equal to the base of natural logarithms.  The constant 'e'
     satisfies the equation 'log' (e) = 1.

     When called with no arguments, return a scalar with the value e.
     When called with a single argument, return a square matrix with the
     dimension specified.  When called with more than one scalar
     argument the first two arguments are taken as the number of rows
     and columns and any further arguments specify additional matrix
     dimensions.  The optional argument CLASS specifies the return type
     and may be either "double" or "single".

     See also: *note log: XREFlog, *note exp: XREFexp, *note pi: XREFpi,
     *note I: XREFI.

 -- Built-in Function: pi
 -- Built-in Function: pi (N)
 -- Built-in Function: pi (N, M)
 -- Built-in Function: pi (N, M, K, ...)
 -- Built-in Function: pi (..., CLASS)
     Return a scalar, matrix, or N-dimensional array whose elements are
     all equal to the ratio of the circumference of a circle to its
     diameter.  Internally, 'pi' is computed as '4.0 * atan (1.0)'.

     When called with no arguments, return a scalar with the value of
     pi.  When called with a single argument, return a square matrix
     with the dimension specified.  When called with more than one
     scalar argument the first two arguments are taken as the number of
     rows and columns and any further arguments specify additional
     matrix dimensions.  The optional argument CLASS specifies the
     return type and may be either "double" or "single".

     See also: *note e: XREFe, *note I: XREFI.

 -- Built-in Function: I
 -- Built-in Function: I (N)
 -- Built-in Function: I (N, M)
 -- Built-in Function: I (N, M, K, ...)
 -- Built-in Function: I (..., CLASS)
     Return a scalar, matrix, or N-dimensional array whose elements are
     all equal to the pure imaginary unit, defined as 'sqrt (-1)'.

     I, and its equivalents i, j, and J, are functions so any of the
     names may be reused for other purposes (such as i for a counter
     variable).

     When called with no arguments, return a scalar with the value i.
     When called with a single argument, return a square matrix with the
     dimension specified.  When called with more than one scalar
     argument the first two arguments are taken as the number of rows
     and columns and any further arguments specify additional matrix
     dimensions.  The optional argument CLASS specifies the return type
     and may be either "double" or "single".

     See also: *note e: XREFe, *note pi: XREFpi, *note log: XREFlog,
     *note exp: XREFexp.

 -- Built-in Function: Inf
 -- Built-in Function: Inf (N)
 -- Built-in Function: Inf (N, M)
 -- Built-in Function: Inf (N, M, K, ...)
 -- Built-in Function: Inf (..., CLASS)
     Return a scalar, matrix or N-dimensional array whose elements are
     all equal to the IEEE representation for positive infinity.

     Infinity is produced when results are too large to be represented
     using the the IEEE floating point format for numbers.  Two common
     examples which produce infinity are division by zero and overflow.

          [ 1/0 e^800 ]
          => Inf   Inf

     When called with no arguments, return a scalar with the value
     'Inf'.  When called with a single argument, return a square matrix
     with the dimension specified.  When called with more than one
     scalar argument the first two arguments are taken as the number of
     rows and columns and any further arguments specify additional
     matrix dimensions.  The optional argument CLASS specifies the
     return type and may be either "double" or "single".

     See also: *note isinf: XREFisinf, *note NaN: XREFNaN.

 -- Built-in Function: NaN
 -- Built-in Function: NaN (N)
 -- Built-in Function: NaN (N, M)
 -- Built-in Function: NaN (N, M, K, ...)
 -- Built-in Function: NaN (..., CLASS)
     Return a scalar, matrix, or N-dimensional array whose elements are
     all equal to the IEEE symbol NaN (Not a Number).  NaN is the result
     of operations which do not produce a well defined numerical result.
     Common operations which produce a NaN are arithmetic with infinity
     (Inf - Inf), zero divided by zero (0/0), and any operation
     involving another NaN value (5 + NaN).

     Note that NaN always compares not equal to NaN (NaN != NaN). This
     behavior is specified by the IEEE standard for floating point
     arithmetic.  To find NaN values, use the 'isnan' function.

     When called with no arguments, return a scalar with the value
     'NaN'.  When called with a single argument, return a square matrix
     with the dimension specified.  When called with more than one
     scalar argument the first two arguments are taken as the number of
     rows and columns and any further arguments specify additional
     matrix dimensions.  The optional argument CLASS specifies the
     return type and may be either "double" or "single".

     See also: *note isnan: XREFisnan, *note Inf: XREFInf.

 -- Built-in Function: eps
 -- Built-in Function: eps (X)
 -- Built-in Function: eps (N, M)
 -- Built-in Function: eps (N, M, K, ...)
 -- Built-in Function: eps (..., CLASS)
     Return a scalar, matrix or N-dimensional array whose elements are
     all eps, the machine precision.  More precisely, 'eps' is the
     relative spacing between any two adjacent numbers in the machine's
     floating point system.  This number is obviously system dependent.
     On machines that support IEEE floating point arithmetic, 'eps' is
     approximately 2.2204e-16 for double precision and 1.1921e-07 for
     single precision.

     When called with no arguments, return a scalar with the value 'eps
     (1.0)'.  Given a single argument X, return the distance between X
     and the next largest value.  When called with more than one
     argument the first two arguments are taken as the number of rows
     and columns and any further arguments specify additional matrix
     dimensions.  The optional argument CLASS specifies the return type
     and may be either "double" or "single".

     See also: *note realmax: XREFrealmax, *note realmin: XREFrealmin,
     *note intmax: XREFintmax, *note bitmax: XREFbitmax.

 -- Built-in Function: realmax
 -- Built-in Function: realmax (N)
 -- Built-in Function: realmax (N, M)
 -- Built-in Function: realmax (N, M, K, ...)
 -- Built-in Function: realmax (..., CLASS)
     Return a scalar, matrix or N-dimensional array whose elements are
     all equal to the largest floating point number that is
     representable.  The actual value is system dependent.  On machines
     that support IEEE floating point arithmetic, 'realmax' is
     approximately 1.7977e+308 for double precision and 3.4028e+38 for
     single precision.

     When called with no arguments, return a scalar with the value
     'realmax ("double")'.  When called with a single argument, return a
     square matrix with the dimension specified.  When called with more
     than one scalar argument the first two arguments are taken as the
     number of rows and columns and any further arguments specify
     additional matrix dimensions.  The optional argument CLASS
     specifies the return type and may be either "double" or "single".

     See also: *note realmin: XREFrealmin, *note intmax: XREFintmax,
     *note bitmax: XREFbitmax, *note eps: XREFeps.

 -- Built-in Function: realmin
 -- Built-in Function: realmin (N)
 -- Built-in Function: realmin (N, M)
 -- Built-in Function: realmin (N, M, K, ...)
 -- Built-in Function: realmin (..., CLASS)
     Return a scalar, matrix or N-dimensional array whose elements are
     all equal to the smallest normalized floating point number that is
     representable.  The actual value is system dependent.  On machines
     that support IEEE floating point arithmetic, 'realmin' is
     approximately 2.2251e-308 for double precision and 1.1755e-38 for
     single precision.

     When called with no arguments, return a scalar with the value
     'realmin ("double")'.  When called with a single argument, return a
     square matrix with the dimension specified.  When called with more
     than one scalar argument the first two arguments are taken as the
     number of rows and columns and any further arguments specify
     additional matrix dimensions.  The optional argument CLASS
     specifies the return type and may be either "double" or "single".

     See also: *note realmax: XREFrealmax, *note intmin: XREFintmin,
     *note eps: XREFeps.


File: octave.info,  Node: Linear Algebra,  Next: Vectorization and Faster Code Execution,  Prev: Arithmetic,  Up: Top

18 Linear Algebra
*****************

This chapter documents the linear algebra functions provided in Octave.
Reference material for many of these functions may be found in Golub and
Van Loan, 'Matrix Computations, 2nd Ed.', Johns Hopkins, 1989, and in
the 'LAPACK Users' Guide', SIAM, 1992.  The 'LAPACK Users' Guide' is
available at: 'http://www.netlib.org/lapack/lug/'

   A common text for engineering courses is G. Strang, 'Linear Algebra
and Its Applications, 4th Edition'.  It has become a widespread
reference for linear algebra.  An alternative is P. Lax 'Linear Algebra
and Its Applications', and also is a good choice.  It claims to be
suitable for high school students with substantial mathematical
interests as well as first-year undergraduates.

* Menu:

* Techniques Used for Linear Algebra::
* Basic Matrix Functions::
* Matrix Factorizations::
* Functions of a Matrix::
* Specialized Solvers::


File: octave.info,  Node: Techniques Used for Linear Algebra,  Next: Basic Matrix Functions,  Up: Linear Algebra

18.1 Techniques Used for Linear Algebra
=======================================

Octave includes a polymorphic solver that selects an appropriate matrix
factorization depending on the properties of the matrix itself.
Generally, the cost of determining the matrix type is small relative to
the cost of factorizing the matrix itself.  In any case the matrix type
is cached once it is calculated so that it is not re-determined each
time it is used in a linear equation.

   The selection tree for how the linear equation is solved or a matrix
inverse is formed is given by:

  1. If the matrix is upper or lower triangular sparse use a forward or
     backward substitution using the LAPACK xTRTRS function, and goto 4.

  2. If the matrix is square, Hermitian with a real positive diagonal,
     attempt Cholesky factorization using the LAPACK xPOTRF function.

  3. If the Cholesky factorization failed or the matrix is not Hermitian
     with a real positive diagonal, and the matrix is square, factorize
     using the LAPACK xGETRF function.

  4. If the matrix is not square, or any of the previous solvers flags a
     singular or near singular matrix, find a least squares solution
     using the LAPACK xGELSD function.

   The user can force the type of the matrix with the 'matrix_type'
function.  This overcomes the cost of discovering the type of the
matrix.  However, it should be noted that identifying the type of the
matrix incorrectly will lead to unpredictable results, and so
'matrix_type' should be used with care.

   It should be noted that the test for whether a matrix is a candidate
for Cholesky factorization, performed above, and by the 'matrix_type'
function, does not make certain that the matrix is Hermitian.  However,
the attempt to factorize the matrix will quickly detect a non-Hermitian
matrix.


File: octave.info,  Node: Basic Matrix Functions,  Next: Matrix Factorizations,  Prev: Techniques Used for Linear Algebra,  Up: Linear Algebra

18.2 Basic Matrix Functions
===========================

 -- Built-in Function: AA = balance (A)
 -- Built-in Function: AA = balance (A, OPT)
 -- Built-in Function: [DD, AA] = balance (A, OPT)
 -- Built-in Function: [D, P, AA] = balance (A, OPT)
 -- Built-in Function: [CC, DD, AA, BB] = balance (A, B, OPT)

     Compute 'AA = DD \ A * DD' in which AA is a matrix whose row and
     column norms are roughly equal in magnitude, and 'DD = P * D', in
     which P is a permutation matrix and D is a diagonal matrix of
     powers of two.  This allows the equilibration to be computed
     without round-off.  Results of eigenvalue calculation are typically
     improved by balancing first.

     If two output values are requested, 'balance' returns the diagonal
     D and the permutation P separately as vectors.  In this case, 'DD =
     eye(n)(:,P) * diag (D)', where n is the matrix size.

     If four output values are requested, compute 'AA = CC*A*DD' and 'BB
     = CC*B*DD', in which AA and BB have nonzero elements of
     approximately the same magnitude and CC and DD are permuted
     diagonal matrices as in DD for the algebraic eigenvalue problem.

     The eigenvalue balancing option OPT may be one of:

     "noperm", "S"
          Scale only; do not permute.

     "noscal", "P"
          Permute only; do not scale.

     Algebraic eigenvalue balancing uses standard LAPACK routines.

     Generalized eigenvalue problem balancing uses Ward's algorithm
     (SIAM Journal on Scientific and Statistical Computing, 1981).

 -- Function File: BW = bandwidth (A, TYPE)
 -- Function File: [LOWER, UPPER] = bandwidth (A)
     Compute the bandwidth of A.

     The TYPE argument is the string "lower" for the lower bandwidth and
     "upper" for the upper bandwidth.  If no TYPE is specified return
     both the lower and upper bandwidth of A.

     The lower/upper bandwidth of a matrix is the number of
     subdiagonals/superdiagonals with nonzero entries.

     See also: *note isbanded: XREFisbanded, *note isdiag: XREFisdiag,
     *note istril: XREFistril, *note istriu: XREFistriu.

 -- Function File: cond (A)
 -- Function File: cond (A, P)
     Compute the P-norm condition number of a matrix.

     'cond (A)' is defined as 'norm (A, P) * norm (inv (A), P)'.

     By default, 'P = 2' is used which implies a (relatively slow)
     singular value decomposition.  Other possible selections are 'P =
     1, Inf, "fro"' which are generally faster.  See 'norm' for a full
     discussion of possible P values.

     The condition number of a matrix quantifies the sensitivity of the
     matrix inversion operation when small changes are made to matrix
     elements.  Ideally the condition number will be close to 1.  When
     the number is large this indicates small changes (such as underflow
     or round-off error) will produce large changes in the resulting
     output.  In such cases the solution results from numerical
     computing are not likely to be accurate.

     See also: *note condest: XREFcondest, *note rcond: XREFrcond, *note
     norm: XREFnorm, *note svd: XREFsvd.

 -- Built-in Function: det (A)
 -- Built-in Function: [D, RCOND] = det (A)
     Compute the determinant of A.

     Return an estimate of the reciprocal condition number if requested.

     Routines from LAPACK are used for full matrices and code from
     UMFPACK is used for sparse matrices.

     The determinant should not be used to check a matrix for
     singularity.  For that, use any of the condition number functions:
     'cond', 'condest', 'rcond'.

     See also: *note cond: XREFcond, *note condest: XREFcondest, *note
     rcond: XREFrcond.

 -- Built-in Function: LAMBDA = eig (A)
 -- Built-in Function: LAMBDA = eig (A, B)
 -- Built-in Function: [V, LAMBDA] = eig (A)
 -- Built-in Function: [V, LAMBDA] = eig (A, B)
     Compute the eigenvalues (and optionally the eigenvectors) of a
     matrix or a pair of matrices

     The algorithm used depends on whether there are one or two input
     matrices, if they are real or complex and if they are symmetric
     (Hermitian if complex) or non-symmetric.

     The eigenvalues returned by 'eig' are not ordered.

     See also: *note eigs: XREFeigs, *note svd: XREFsvd.

 -- Built-in Function: G = givens (X, Y)
 -- Built-in Function: [C, S] = givens (X, Y)
     Compute the Givens rotation matrix G.

     The Givens matrix is a 2 by 2 orthogonal matrix

     'G = [C S; -S' C]'

     such that

     'G [X; Y] = [*; 0]'

     with X and Y scalars.

     If two output arguments are requested, return the factors C and S
     rather than the Givens rotation matrix.

     For example:

          givens (1, 1)
             =>   0.70711   0.70711
                 -0.70711   0.70711

     See also: *note planerot: XREFplanerot.

 -- Function File: [G, Y] = planerot (X)
     Given a two-element column vector, return the 2 by 2 orthogonal
     matrix G such that 'Y = G * X' and 'Y(2) = 0'.

     See also: *note givens: XREFgivens.

 -- Built-in Function: X = inv (A)
 -- Built-in Function: [X, RCOND] = inv (A)
     Compute the inverse of the square matrix A.  Return an estimate of
     the reciprocal condition number if requested, otherwise warn of an
     ill-conditioned matrix if the reciprocal condition number is small.

     In general it is best to avoid calculating the inverse of a matrix
     directly.  For example, it is both faster and more accurate to
     solve systems of equations (A*x = b) with 'Y = A \ b', rather than
     'Y = inv (A) * b'.

     If called with a sparse matrix, then in general X will be a full
     matrix requiring significantly more storage.  Avoid forming the
     inverse of a sparse matrix if possible.

     See also: *note ldivide: XREFldivide, *note rdivide: XREFrdivide.

 -- Function File: X = linsolve (A, B)
 -- Function File: X = linsolve (A, B, OPTS)
 -- Function File: [X, R] = linsolve (...)
     Solve the linear system 'A*x = b'.

     With no options, this function is equivalent to the left division
     operator ('x = A \ b') or the matrix-left-divide function
     ('x = mldivide (A, b)').

     Octave ordinarily examines the properties of the matrix A and
     chooses a solver that best matches the matrix.  By passing a
     structure OPTS to 'linsolve' you can inform Octave directly about
     the matrix A.  In this case Octave will skip the matrix examination
     and proceed directly to solving the linear system.

     *Warning:* If the matrix A does not have the properties listed in
     the OPTS structure then the result will not be accurate AND no
     warning will be given.  When in doubt, let Octave examine the
     matrix and choose the appropriate solver as this step takes little
     time and the result is cached so that it is only done once per
     linear system.

     Possible OPTS fields (set value to true/false):

     LT
          A is lower triangular

     UT
          A is upper triangular

     UHESS
          A is upper Hessenberg (currently makes no difference)

     SYM
          A is symmetric or complex Hermitian (currently makes no
          difference)

     POSDEF
          A is positive definite

     RECT
          A is general rectangular (currently makes no difference)

     TRANSA
          Solve 'A'*x = b' by 'transpose (A) \ b'

     The optional second output R is the inverse condition number of A
     (zero if matrix is singular).

     See also: *note mldivide: XREFmldivide, *note matrix_type:
     XREFmatrix_type, *note rcond: XREFrcond.

 -- Built-in Function: TYPE = matrix_type (A)
 -- Built-in Function: TYPE = matrix_type (A, "nocompute")
 -- Built-in Function: A = matrix_type (A, TYPE)
 -- Built-in Function: A = matrix_type (A, "upper", PERM)
 -- Built-in Function: A = matrix_type (A, "lower", PERM)
 -- Built-in Function: A = matrix_type (A, "banded", NL, NU)
     Identify the matrix type or mark a matrix as a particular type.
     This allows more rapid solutions of linear equations involving A to
     be performed.  Called with a single argument, 'matrix_type' returns
     the type of the matrix and caches it for future use.  Called with
     more than one argument, 'matrix_type' allows the type of the matrix
     to be defined.

     If the option "nocompute" is given, the function will not attempt
     to guess the type if it is still unknown.  This is useful for
     debugging purposes.

     The possible matrix types depend on whether the matrix is full or
     sparse, and can be one of the following

     "unknown"
          Remove any previously cached matrix type, and mark type as
          unknown.

     "full"
          Mark the matrix as full.

     "positive definite"
          Probable full positive definite matrix.

     "diagonal"
          Diagonal matrix.  (Sparse matrices only)

     "permuted diagonal"
          Permuted Diagonal matrix.  The permutation does not need to be
          specifically indicated, as the structure of the matrix
          explicitly gives this.  (Sparse matrices only)

     "upper"
          Upper triangular.  If the optional third argument PERM is
          given, the matrix is assumed to be a permuted upper triangular
          with the permutations defined by the vector PERM.

     "lower"
          Lower triangular.  If the optional third argument PERM is
          given, the matrix is assumed to be a permuted lower triangular
          with the permutations defined by the vector PERM.

     "banded"
     "banded positive definite"
          Banded matrix with the band size of NL below the diagonal and
          NU above it.  If NL and NU are 1, then the matrix is
          tridiagonal and treated with specialized code.  In addition
          the matrix can be marked as probably a positive definite.
          (Sparse matrices only)

     "singular"
          The matrix is assumed to be singular and will be treated with
          a minimum norm solution.

     Note that the matrix type will be discovered automatically on the
     first attempt to solve a linear equation involving A.  Therefore
     'matrix_type' is only useful to give Octave hints of the matrix
     type.  Incorrectly defining the matrix type will result in
     incorrect results from solutions of linear equations; it is
     entirely *the responsibility of the user* to correctly identify the
     matrix type.

     Also, the test for positive definiteness is a low-cost test for a
     Hermitian matrix with a real positive diagonal.  This does not
     guarantee that the matrix is positive definite, but only that it is
     a probable candidate.  When such a matrix is factorized, a
     Cholesky factorization is first attempted, and if that fails the
     matrix is then treated with an LU factorization.  Once the matrix
     has been factorized, 'matrix_type' will return the correct
     classification of the matrix.

 -- Built-in Function: norm (A)
 -- Built-in Function: norm (A, P)
 -- Built-in Function: norm (A, P, OPT)
     Compute the p-norm of the matrix A.  If the second argument is
     missing, 'p = 2' is assumed.

     If A is a matrix (or sparse matrix):

     P = '1'
          1-norm, the largest column sum of the absolute values of A.

     P = '2'
          Largest singular value of A.

     P = 'Inf' or "inf"
          Infinity norm, the largest row sum of the absolute values of
          A.

     P = "fro"
          Frobenius norm of A, 'sqrt (sum (diag (A' * A)))'.

     other P, 'P > 1'
          maximum 'norm (A*x, p)' such that 'norm (x, p) == 1'

     If A is a vector or a scalar:

     P = 'Inf' or "inf"
          'max (abs (A))'.

     P = '-Inf'
          'min (abs (A))'.

     P = "fro"
          Frobenius norm of A, 'sqrt (sumsq (abs (A)))'.

     P = 0
          Hamming norm - the number of nonzero elements.

     other P, 'P > 1'
          p-norm of A, '(sum (abs (A) .^ P)) ^ (1/P)'.

     other P 'P < 1'
          the p-pseudonorm defined as above.

     If OPT is the value "rows", treat each row as a vector and compute
     its norm.  The result is returned as a column vector.  Similarly,
     if OPT is "columns" or "cols" then compute the norms of each column
     and return a row vector.

     See also: *note cond: XREFcond, *note svd: XREFsvd.

 -- Function File: null (A)
 -- Function File: null (A, TOL)
     Return an orthonormal basis of the null space of A.

     The dimension of the null space is taken as the number of singular
     values of A not greater than TOL.  If the argument TOL is missing,
     it is computed as

          max (size (A)) * max (svd (A)) * eps

     See also: *note orth: XREForth.

 -- Function File: orth (A)
 -- Function File: orth (A, TOL)
     Return an orthonormal basis of the range space of A.

     The dimension of the range space is taken as the number of singular
     values of A greater than TOL.  If the argument TOL is missing, it
     is computed as

          max (size (A)) * max (svd (A)) * eps

     See also: *note null: XREFnull.

 -- Built-in Function: [Y, H] = mgorth (X, V)
     Orthogonalize a given column vector X with respect to a set of
     orthonormal vectors comprising the columns of V using the modified
     Gram-Schmidt method.  On exit, Y is a unit vector such that:

            norm (Y) = 1
            V' * Y = 0
            X = [V, Y]*H'

 -- Built-in Function: pinv (X)
 -- Built-in Function: pinv (X, TOL)
     Return the pseudoinverse of X.  Singular values less than TOL are
     ignored.

     If the second argument is omitted, it is taken to be

          tol = max (size (X)) * sigma_max (X) * eps,

     where 'sigma_max (X)' is the maximal singular value of X.

 -- Function File: rank (A)
 -- Function File: rank (A, TOL)
     Compute the rank of matrix A, using the singular value
     decomposition.

     The rank is taken to be the number of singular values of A that are
     greater than the specified tolerance TOL.  If the second argument
     is omitted, it is taken to be

          tol = max (size (A)) * sigma(1) * eps;

     where 'eps' is machine precision and 'sigma(1)' is the largest
     singular value of A.

     The rank of a matrix is the number of linearly independent rows or
     columns and determines how many particular solutions exist to a
     system of equations.  Use 'null' for finding the remaining
     homogenous solutions.

     Example:

          x = [1 2 3
               4 5 6
               7 8 9];
          rank (x)
            => 2

     The number of linearly independent rows is only 2 because the final
     row is a linear combination of -1*row1 + 2*row2.

     See also: *note null: XREFnull, *note sprank: XREFsprank, *note
     svd: XREFsvd.

 -- Built-in Function: C = rcond (A)
     Compute the 1-norm estimate of the reciprocal condition number as
     returned by LAPACK.  If the matrix is well-conditioned then C will
     be near 1 and if the matrix is poorly conditioned it will be close
     to zero.

     The matrix A must not be sparse.  If the matrix is sparse then
     'condest (A)' or 'rcond (full (A))' should be used instead.

     See also: *note cond: XREFcond, *note condest: XREFcondest.

 -- Function File: trace (A)
     Compute the trace of A, the sum of the elements along the main
     diagonal.

     The implementation is straightforward: 'sum (diag (A))'.

     See also: *note eig: XREFeig.

 -- Function File: rref (A)
 -- Function File: rref (A, TOL)
 -- Function File: [R, K] = rref (...)
     Return the reduced row echelon form of A.  TOL defaults to 'eps *
     max (size (A)) * norm (A, inf)'.

     Called with two return arguments, K returns the vector of "bound
     variables", which are those columns on which elimination has been
     performed.


File: octave.info,  Node: Matrix Factorizations,  Next: Functions of a Matrix,  Prev: Basic Matrix Functions,  Up: Linear Algebra

18.3 Matrix Factorizations
==========================

 -- Loadable Function: R = chol (A)
 -- Loadable Function: [R, P] = chol (A)
 -- Loadable Function: [R, P, Q] = chol (S)
 -- Loadable Function: [R, P, Q] = chol (S, "vector")
 -- Loadable Function: [L, ...] = chol (..., "lower")
 -- Loadable Function: [L, ...] = chol (..., "upper")
     Compute the Cholesky factor, R, of the symmetric positive definite
     matrix A, where

          R' * R = A.

     Called with one output argument 'chol' fails if A or S is not
     positive definite.  With two or more output arguments P flags
     whether the matrix was positive definite and 'chol' does not fail.
     A zero value indicated that the matrix was positive definite and
     the R gives the factorization, and P will have a positive value
     otherwise.

     If called with 3 outputs then a sparsity preserving row/column
     permutation is applied to A prior to the factorization.  That is R
     is the factorization of 'A(Q,Q)' such that

          R' * R = Q' * A * Q.

     The sparsity preserving permutation is generally returned as a
     matrix.  However, given the flag "vector", Q will be returned as a
     vector such that

          R' * R = A(Q, Q).

     Called with either a sparse or full matrix and using the "lower"
     flag, 'chol' returns the lower triangular factorization such that

          L * L' = A.

     For full matrices, if the "lower" flag is set only the lower
     triangular part of the matrix is used for the factorization,
     otherwise the upper triangular part is used.

     In general the lower triangular factorization is significantly
     faster for sparse matrices.

     See also: *note hess: XREFhess, *note lu: XREFlu, *note qr: XREFqr,
     *note qz: XREFqz, *note schur: XREFschur, *note svd: XREFsvd, *note
     ichol: XREFichol, *note cholinv: XREFcholinv, *note chol2inv:
     XREFchol2inv, *note cholupdate: XREFcholupdate, *note cholinsert:
     XREFcholinsert, *note choldelete: XREFcholdelete, *note cholshift:
     XREFcholshift.

 -- Loadable Function: cholinv (A)
     Use the Cholesky factorization to compute the inverse of the
     symmetric positive definite matrix A.

     See also: *note chol: XREFchol, *note chol2inv: XREFchol2inv, *note
     inv: XREFinv.

 -- Loadable Function: chol2inv (U)
     Invert a symmetric, positive definite square matrix from its
     Cholesky decomposition, U.  Note that U should be an
     upper-triangular matrix with positive diagonal elements.  'chol2inv
     (U)' provides 'inv (U'*U)' but it is much faster than using 'inv'.

     See also: *note chol: XREFchol, *note cholinv: XREFcholinv, *note
     inv: XREFinv.

 -- Loadable Function: [R1, INFO] = cholupdate (R, U, OP)
     Update or downdate a Cholesky factorization.  Given an upper
     triangular matrix R and a column vector U, attempt to determine
     another upper triangular matrix R1 such that

        * R1'*R1 = R'*R + U*U' if OP is "+"

        * R1'*R1 = R'*R - U*U' if OP is "-"

     If OP is "-", INFO is set to

        * 0 if the downdate was successful,

        * 1 if R'*R - U*U' is not positive definite,

        * 2 if R is singular.

     If INFO is not present, an error message is printed in cases 1 and
     2.

     See also: *note chol: XREFchol, *note cholinsert: XREFcholinsert,
     *note choldelete: XREFcholdelete, *note cholshift: XREFcholshift.

 -- Loadable Function: R1 = cholinsert (R, J, U)
 -- Loadable Function: [R1, INFO] = cholinsert (R, J, U)
     Given a Cholesky factorization of a real symmetric or complex
     Hermitian positive definite matrix A = R'*R, R upper triangular,
     return the Cholesky factorization of A1, where A1(p,p) = A, A1(:,j) = A1(j,:)' = u
     and p = [1:j-1,j+1:n+1].  u(j) should be positive.  On return, INFO
     is set to

        * 0 if the insertion was successful,

        * 1 if A1 is not positive definite,

        * 2 if R is singular.

     If INFO is not present, an error message is printed in cases 1 and
     2.

     See also: *note chol: XREFchol, *note cholupdate: XREFcholupdate,
     *note choldelete: XREFcholdelete, *note cholshift: XREFcholshift.

 -- Loadable Function: R1 = choldelete (R, J)
     Given a Cholesky factorization of a real symmetric or complex
     Hermitian positive definite matrix A = R'*R, R upper triangular,
     return the Cholesky factorization of A(p,p), where
     p = [1:j-1,j+1:n+1].

     See also: *note chol: XREFchol, *note cholupdate: XREFcholupdate,
     *note cholinsert: XREFcholinsert, *note cholshift: XREFcholshift.

 -- Loadable Function: R1 = cholshift (R, I, J)
     Given a Cholesky factorization of a real symmetric or complex
     Hermitian positive definite matrix A = R'*R, R upper triangular,
     return the Cholesky factorization of A(p,p), where p is the
     permutation
     'p = [1:i-1, shift(i:j, 1), j+1:n]' if I < J
     or
     'p = [1:j-1, shift(j:i,-1), i+1:n]' if J < I.

     See also: *note chol: XREFchol, *note cholupdate: XREFcholupdate,
     *note cholinsert: XREFcholinsert, *note choldelete: XREFcholdelete.

 -- Built-in Function: H = hess (A)
 -- Built-in Function: [P, H] = hess (A)
     Compute the Hessenberg decomposition of the matrix A.

     The Hessenberg decomposition is 'P * H * P' = A' where P is a
     square unitary matrix ('P' * P = I', using complex-conjugate
     transposition) and H is upper Hessenberg ('H(i, j) = 0 forall i >=
     j+1)'.

     The Hessenberg decomposition is usually used as the first step in
     an eigenvalue computation, but has other applications as well (see
     Golub, Nash, and Van Loan, IEEE Transactions on Automatic Control,
     1979).

     See also: *note eig: XREFeig, *note chol: XREFchol, *note lu:
     XREFlu, *note qr: XREFqr, *note qz: XREFqz, *note schur: XREFschur,
     *note svd: XREFsvd.

 -- Built-in Function: [L, U] = lu (A)
 -- Built-in Function: [L, U, P] = lu (A)
 -- Built-in Function: [L, U, P, Q] = lu (S)
 -- Built-in Function: [L, U, P, Q, R] = lu (S)
 -- Built-in Function: [...] = lu (S, THRES)
 -- Built-in Function: Y = lu (...)
 -- Built-in Function: [...] = lu (..., "vector")
     Compute the LU decomposition of A.  If A is full subroutines from
     LAPACK are used and if A is sparse then UMFPACK is used.  The
     result is returned in a permuted form, according to the optional
     return value P.  For example, given the matrix 'a = [1, 2; 3, 4]',

          [l, u, p] = lu (A)

     returns

          l =

            1.00000  0.00000
            0.33333  1.00000

          u =

            3.00000  4.00000
            0.00000  0.66667

          p =

            0  1
            1  0

     The matrix is not required to be square.

     When called with two or three output arguments and a spare input
     matrix, 'lu' does not attempt to perform sparsity preserving column
     permutations.  Called with a fourth output argument, the sparsity
     preserving column transformation Q is returned, such that 'P * A *
     Q = L * U'.

     Called with a fifth output argument and a sparse input matrix, 'lu'
     attempts to use a scaling factor R on the input matrix such that 'P
     * (R \ A) * Q = L * U'.  This typically leads to a sparser and more
     stable factorization.

     An additional input argument THRES, that defines the pivoting
     threshold can be given.  THRES can be a scalar, in which case it
     defines the UMFPACK pivoting tolerance for both symmetric and
     unsymmetric cases.  If THRES is a 2-element vector, then the first
     element defines the pivoting tolerance for the unsymmetric UMFPACK
     pivoting strategy and the second for the symmetric strategy.  By
     default, the values defined by 'spparms' are used ([0.1, 0.001]).

     Given the string argument "vector", 'lu' returns the values of P
     and Q as vector values, such that for full matrix, 'A (P,:) = L *
     U', and 'R(P,:) * A (:, Q) = L * U'.

     With two output arguments, returns the permuted forms of the upper
     and lower triangular matrices, such that 'A = L * U'.  With one
     output argument Y, then the matrix returned by the LAPACK routines
     is returned.  If the input matrix is sparse then the matrix L is
     embedded into U to give a return value similar to the full case.
     For both full and sparse matrices, 'lu' loses the permutation
     information.

     See also: *note luupdate: XREFluupdate, *note ilu: XREFilu, *note
     chol: XREFchol, *note hess: XREFhess, *note qr: XREFqr, *note qz:
     XREFqz, *note schur: XREFschur, *note svd: XREFsvd.

 -- Built-in Function: [L, U] = luupdate (L, U, X, Y)
 -- Built-in Function: [L, U, P] = luupdate (L, U, P, X, Y)
     Given an LU factorization of a real or complex matrix A = L*U,
     L lower unit trapezoidal and U upper trapezoidal, return the
     LU factorization of A + X*Y.', where X and Y are column vectors
     (rank-1 update) or matrices with equal number of columns (rank-k
     update).  Optionally, row-pivoted updating can be used by supplying
     a row permutation (pivoting) matrix P; in that case, an updated
     permutation matrix is returned.  Note that if L, U, P is a pivoted
     LU factorization as obtained by 'lu':

          [L, U, P] = lu (A);

     then a factorization of A+X*Y.'  can be obtained either as

          [L1, U1] = lu (L, U, P*X, Y)

     or

          [L1, U1, P1] = lu (L, U, P, X, Y)

     The first form uses the unpivoted algorithm, which is faster, but
     less stable.  The second form uses a slower pivoted algorithm,
     which is more stable.

     The matrix case is done as a sequence of rank-1 updates; thus, for
     large enough k, it will be both faster and more accurate to
     recompute the factorization from scratch.

     See also: *note lu: XREFlu, *note cholupdate: XREFcholupdate, *note
     qrupdate: XREFqrupdate.

 -- Loadable Function: [Q, R, P] = qr (A)
 -- Loadable Function: [Q, R, P] = qr (A, '0')
 -- Loadable Function: [C, R] = qr (A, B)
 -- Loadable Function: [C, R] = qr (A, B, '0')
     Compute the QR factorization of A, using standard LAPACK
     subroutines.  For example, given the matrix 'A = [1, 2; 3, 4]',

          [Q, R] = qr (A)

     returns

          Q =

            -0.31623  -0.94868
            -0.94868   0.31623

          R =

            -3.16228  -4.42719
             0.00000  -0.63246

     The 'qr' factorization has applications in the solution of least
     squares problems

          min norm(A x - b)

     for overdetermined systems of equations (i.e., A is a tall, thin
     matrix).  The QR factorization is 'Q * R = A' where Q is an
     orthogonal matrix and R is upper triangular.

     If given a second argument of '0', 'qr' returns an economy-sized
     QR factorization, omitting zero rows of R and the corresponding
     columns of Q.

     If the matrix A is full, the permuted QR factorization '[Q, R, P] =
     qr (A)' forms the QR factorization such that the diagonal entries
     of R are decreasing in magnitude order.  For example, given the
     matrix 'a = [1, 2; 3, 4]',

          [Q, R, P] = qr (A)

     returns

          Q =

            -0.44721  -0.89443
            -0.89443   0.44721

          R =

            -4.47214  -3.13050
             0.00000   0.44721

          P =

             0  1
             1  0

     The permuted 'qr' factorization '[Q, R, P] = qr (A)' factorization
     allows the construction of an orthogonal basis of 'span (A)'.

     If the matrix A is sparse, then compute the sparse QR factorization
     of A, using CSPARSE.  As the matrix Q is in general a full matrix,
     this function returns the Q-less factorization R of A, such that 'R
     = chol (A' * A)'.

     If the final argument is the scalar '0' and the number of rows is
     larger than the number of columns, then an economy factorization is
     returned.  That is R will have only 'size (A,1)' rows.

     If an additional matrix B is supplied, then 'qr' returns C, where
     'C = Q' * B'.  This allows the least squares approximation of 'A \
     B' to be calculated as

          [C, R] = qr (A, B)
          x = R \ C

     See also: *note chol: XREFchol, *note hess: XREFhess, *note lu:
     XREFlu, *note qz: XREFqz, *note schur: XREFschur, *note svd:
     XREFsvd, *note qrupdate: XREFqrupdate, *note qrinsert:
     XREFqrinsert, *note qrdelete: XREFqrdelete, *note qrshift:
     XREFqrshift.

 -- Loadable Function: [Q1, R1] = qrupdate (Q, R, U, V)
     Given a QR factorization of a real or complex matrix A = Q*R,
     Q unitary and R upper trapezoidal, return the QR factorization of
     A + U*V', where U and V are column vectors (rank-1 update) or
     matrices with equal number of columns (rank-k update).  Notice that
     the latter case is done as a sequence of rank-1 updates; thus, for
     k large enough, it will be both faster and more accurate to
     recompute the factorization from scratch.

     The QR factorization supplied may be either full (Q is square) or
     economized (R is square).

     See also: *note qr: XREFqr, *note qrinsert: XREFqrinsert, *note
     qrdelete: XREFqrdelete, *note qrshift: XREFqrshift.

 -- Loadable Function: [Q1, R1] = qrinsert (Q, R, J, X, ORIENT)
     Given a QR factorization of a real or complex matrix A = Q*R,
     Q unitary and R upper trapezoidal, return the QR factorization of
     [A(:,1:j-1) x A(:,j:n)], where U is a column vector to be inserted
     into A (if ORIENT is "col"), or the QR factorization of
     [A(1:j-1,:);x;A(:,j:n)], where X is a row vector to be inserted
     into A (if ORIENT is "row").

     The default value of ORIENT is "col".  If ORIENT is "col", U may be
     a matrix and J an index vector resulting in the QR factorization of
     a matrix B such that B(:,J) gives U and B(:,J) = [] gives A.
     Notice that the latter case is done as a sequence of k insertions;
     thus, for k large enough, it will be both faster and more accurate
     to recompute the factorization from scratch.

     If ORIENT is "col", the QR factorization supplied may be either
     full (Q is square) or economized (R is square).

     If ORIENT is "row", full factorization is needed.

     See also: *note qr: XREFqr, *note qrupdate: XREFqrupdate, *note
     qrdelete: XREFqrdelete, *note qrshift: XREFqrshift.

 -- Loadable Function: [Q1, R1] = qrdelete (Q, R, J, ORIENT)
     Given a QR factorization of a real or complex matrix A = Q*R,
     Q unitary and R upper trapezoidal, return the QR factorization of
     [A(:,1:j-1) A(:,j+1:n)], i.e., A with one column deleted (if ORIENT
     is "col"), or the QR factorization of [A(1:j-1,:);A(j+1:n,:)],
     i.e., A with one row deleted (if ORIENT is "row").

     The default value of ORIENT is "col".

     If ORIENT is "col", J may be an index vector resulting in the
     QR factorization of a matrix B such that A(:,J) = [] gives B.
     Notice that the latter case is done as a sequence of k deletions;
     thus, for k large enough, it will be both faster and more accurate
     to recompute the factorization from scratch.

     If ORIENT is "col", the QR factorization supplied may be either
     full (Q is square) or economized (R is square).

     If ORIENT is "row", full factorization is needed.

     See also: *note qr: XREFqr, *note qrupdate: XREFqrupdate, *note
     qrinsert: XREFqrinsert, *note qrshift: XREFqrshift.

 -- Loadable Function: [Q1, R1] = qrshift (Q, R, I, J)
     Given a QR factorization of a real or complex matrix A = Q*R,
     Q unitary and R upper trapezoidal, return the QR factorization of
     A(:,p), where p is the permutation
     'p = [1:i-1, shift(i:j, 1), j+1:n]' if I < J
     or
     'p = [1:j-1, shift(j:i,-1), i+1:n]' if J < I.

     See also: *note qr: XREFqr, *note qrupdate: XREFqrupdate, *note
     qrinsert: XREFqrinsert, *note qrdelete: XREFqrdelete.

 -- Built-in Function: LAMBDA = qz (A, B)
 -- Built-in Function: LAMBDA = qz (A, B, OPT)
     QZ decomposition of the generalized eigenvalue problem (A x = s B
     x).  There are three ways to call this function:
       1. 'LAMBDA = qz (A, B)'

          Computes the generalized eigenvalues LAMBDA of (A - s B).

       2. '[AA, BB, Q, Z, V, W, LAMBDA] = qz (A, B)'

          Computes QZ decomposition, generalized eigenvectors, and
          generalized eigenvalues of (A - s B)


               A * V = B * V * diag (LAMBDA)
               W' * A = diag (LAMBDA) * W' * B
               AA = Q * A * Z, BB = Q * B * Z


          with Q and Z orthogonal (unitary)= I

       3. '[AA,BB,Z{, LAMBDA}] = qz (A, B, OPT)'

          As in form [2], but allows ordering of generalized eigenpairs
          for (e.g.)  solution of discrete time algebraic Riccati
          equations.  Form 3 is not available for complex matrices, and
          does not compute the generalized eigenvectors V, W, nor the
          orthogonal matrix Q.

          OPT
               for ordering eigenvalues of the GEP pencil.  The leading
               block of the revised pencil contains all eigenvalues that
               satisfy:

               "N"
                    = unordered (default)

               "S"
                    = small: leading block has all |lambda| <= 1

               "B"
                    = big: leading block has all |lambda| >= 1

               "-"
                    = negative real part: leading block has all
                    eigenvalues in the open left half-plane

               "+"
                    = non-negative real part: leading block has all
                    eigenvalues in the closed right half-plane

     Note: 'qz' performs permutation balancing, but not scaling (*note
     XREFbalance::).  The order of output arguments was selected for
     compatibility with MATLAB.

     See also: *note eig: XREFeig, *note balance: XREFbalance, *note lu:
     XREFlu, *note chol: XREFchol, *note hess: XREFhess, *note qr:
     XREFqr, *note qzhess: XREFqzhess, *note schur: XREFschur, *note
     svd: XREFsvd.

 -- Function File: [AA, BB, Q, Z] = qzhess (A, B)
     Compute the Hessenberg-triangular decomposition of the matrix
     pencil '(A, B)', returning 'AA = Q * A * Z', 'BB = Q * B * Z', with
     Q and Z orthogonal.  For example:

          [aa, bb, q, z] = qzhess ([1, 2; 3, 4], [5, 6; 7, 8])
               => aa = [ -3.02244, -4.41741;  0.92998,  0.69749 ]
               => bb = [ -8.60233, -9.99730;  0.00000, -0.23250 ]
               =>  q = [ -0.58124, -0.81373; -0.81373,  0.58124 ]
               =>  z = [ 1, 0; 0, 1 ]

     The Hessenberg-triangular decomposition is the first step in Moler
     and Stewart's QZ decomposition algorithm.

     Algorithm taken from Golub and Van Loan, 'Matrix Computations, 2nd
     edition'.

     See also: *note lu: XREFlu, *note chol: XREFchol, *note hess:
     XREFhess, *note qr: XREFqr, *note qz: XREFqz, *note schur:
     XREFschur, *note svd: XREFsvd.

 -- Built-in Function: S = schur (A)
 -- Built-in Function: S = schur (A, "real")
 -- Built-in Function: S = schur (A, "complex")
 -- Built-in Function: S = schur (A, OPT)
 -- Built-in Function: [U, S] = schur (...)
     Compute the Schur decomposition of A

          S = U' * A * U

     where U is a unitary matrix ('U'* U' is identity) and S is upper
     triangular.  The eigenvalues of A (and S) are the diagonal elements
     of S.  If the matrix A is real, then the real Schur decomposition
     is computed, in which the matrix U is orthogonal and S is block
     upper triangular with blocks of size at most '2 x 2' along the
     diagonal.  The diagonal elements of S (or the eigenvalues of the '2
     x 2' blocks, when appropriate) are the eigenvalues of A and S.

     The default for real matrices is a real Schur decomposition.  A
     complex decomposition may be forced by passing the flag "complex".

     The eigenvalues are optionally ordered along the diagonal according
     to the value of OPT.  'OPT = "a"' indicates that all eigenvalues
     with negative real parts should be moved to the leading block of S
     (used in 'are'), 'OPT = "d"' indicates that all eigenvalues with
     magnitude less than one should be moved to the leading block of S
     (used in 'dare'), and 'OPT = "u"', the default, indicates that no
     ordering of eigenvalues should occur.  The leading K columns of U
     always span the A-invariant subspace corresponding to the K leading
     eigenvalues of S.

     The Schur decomposition is used to compute eigenvalues of a square
     matrix, and has applications in the solution of algebraic Riccati
     equations in control (see 'are' and 'dare').

     See also: *note rsf2csf: XREFrsf2csf, *note ordschur: XREFordschur,
     *note lu: XREFlu, *note chol: XREFchol, *note hess: XREFhess, *note
     qr: XREFqr, *note qz: XREFqz, *note svd: XREFsvd.

 -- Function File: [U, T] = rsf2csf (UR, TR)
     Convert a real, upper quasi-triangular Schur form TR to a complex,
     upper triangular Schur form T.

     Note that the following relations hold:

     UR * TR * UR' = U * T * U' and 'U' * U' is the identity matrix I.

     Note also that U and T are not unique.

     See also: *note schur: XREFschur.

 -- Loadable Function: [UR, SR] = ordschur (U, S, SELECT)
     Reorders the real Schur factorization (U,S) obtained with the
     'schur' function, so that selected eigenvalues appear in the upper
     left diagonal blocks of the quasi triangular Schur matrix.  The
     logical vector SELECT specifies the selected eigenvalues as they
     appear along S's diagonal.

     For example, given the matrix 'A = [1, 2; 3, 4]', and its Schur
     decomposition

          [U, S] = schur (A)

     which returns

          U =

            -0.82456  -0.56577
             0.56577  -0.82456

          S =

            -0.37228  -1.00000
             0.00000   5.37228


     It is possible to reorder the decomposition so that the positive
     eigenvalue is in the upper left corner, by doing:

          [U, S] = ordschur (U, S, [0,1])

     See also: *note schur: XREFschur.

 -- Function File: ANGLE = subspace (A, B)
     Determine the largest principal angle between two subspaces spanned
     by the columns of matrices A and B.

 -- Built-in Function: S = svd (A)
 -- Built-in Function: [U, S, V] = svd (A)
 -- Built-in Function: [U, S, V] = svd (A, ECON)
     Compute the singular value decomposition of A

          A = U*S*V'

     The function 'svd' normally returns only the vector of singular
     values.  When called with three return values, it computes U, S,
     and V.  For example,

          svd (hilb (3))

     returns

          ans =

            1.4083189
            0.1223271
            0.0026873

     and

          [u, s, v] = svd (hilb (3))

     returns

          u =

            -0.82704   0.54745   0.12766
            -0.45986  -0.52829  -0.71375
            -0.32330  -0.64901   0.68867

          s =

            1.40832  0.00000  0.00000
            0.00000  0.12233  0.00000
            0.00000  0.00000  0.00269

          v =

            -0.82704   0.54745   0.12766
            -0.45986  -0.52829  -0.71375
            -0.32330  -0.64901   0.68867

     If given a second argument, 'svd' returns an economy-sized
     decomposition, eliminating the unnecessary rows or columns of U or
     V.

     See also: *note svd_driver: XREFsvd_driver, *note svds: XREFsvds,
     *note eig: XREFeig, *note lu: XREFlu, *note chol: XREFchol, *note
     hess: XREFhess, *note qr: XREFqr, *note qz: XREFqz.

 -- Built-in Function: VAL = svd_driver ()
 -- Built-in Function: OLD_VAL = svd_driver (NEW_VAL)
 -- Built-in Function: svd_driver (NEW_VAL, "local")
     Query or set the underlying LAPACK driver used by 'svd'.  Currently
     recognized values are "gesvd" and "gesdd".  The default is "gesvd".

     When called from inside a function with the "local" option, the
     variable is changed locally for the function and any subroutines it
     calls.  The original variable value is restored when exiting the
     function.

     See also: *note svd: XREFsvd.

 -- Function File: [HOUSV, BETA, ZER] = housh (X, J, Z)
     Compute Householder reflection vector HOUSV to reflect X to be the
     j-th column of identity, i.e.,

          (I - beta*housv*housv')x =  norm (x)*e(j) if x(j) < 0,
          (I - beta*housv*housv')x = -norm (x)*e(j) if x(j) >= 0

     Inputs

     X
          vector

     J
          index into vector

     Z
          threshold for zero (usually should be the number 0)

     Outputs (see Golub and Van Loan):

     BETA
          If beta = 0, then no reflection need be applied (zer set to 0)

     HOUSV
          householder vector

 -- Function File: [U, H, NU] = krylov (A, V, K, EPS1, PFLG)
     Construct an orthogonal basis U of block Krylov subspace

          [v a*v a^2*v ... a^(k+1)*v]

     Using Householder reflections to guard against loss of
     orthogonality.

     If V is a vector, then H contains the Hessenberg matrix such that
     a*u == u*h+rk*ek', in which 'rk = a*u(:,k)-u*h(:,k)', and ek' is
     the vector '[0, 0, ..., 1]' of length 'k'.  Otherwise, H is
     meaningless.

     If V is a vector and K is greater than 'length (A) - 1', then H
     contains the Hessenberg matrix such that 'a*u == u*h'.

     The value of NU is the dimension of the span of the Krylov subspace
     (based on EPS1).

     If B is a vector and K is greater than M-1, then H contains the
     Hessenberg decomposition of A.

     The optional parameter EPS1 is the threshold for zero.  The default
     value is 1e-12.

     If the optional parameter PFLG is nonzero, row pivoting is used to
     improve numerical behavior.  The default value is 0.

     Reference: A. Hodel, P. Misra, 'Partial Pivoting in the Computation
     of Krylov Subspaces of Large Sparse Systems', Proceedings of the
     42nd IEEE Conference on Decision and Control, December 2003.


File: octave.info,  Node: Functions of a Matrix,  Next: Specialized Solvers,  Prev: Matrix Factorizations,  Up: Linear Algebra

18.4 Functions of a Matrix
==========================

 -- Function File: expm (A)
     Return the exponential of a matrix, defined as the infinite Taylor
     series

          expm (A) = I + A + A^2/2! + A^3/3! + ...

     The Taylor series is _not_ the way to compute the matrix
     exponential; see Moler and Van Loan, 'Nineteen Dubious Ways to
     Compute the Exponential of a Matrix', SIAM Review, 1978.  This
     routine uses Ward's diagonal Pade' approximation method with three
     step preconditioning (SIAM Journal on Numerical Analysis, 1977).
     Diagonal Pade' approximations are rational polynomials of matrices

               -1
          D (A)   N (A)

     whose Taylor series matches the first '2q+1' terms of the Taylor
     series above; direct evaluation of the Taylor series (with the same
     preconditioning steps) may be desirable in lieu of the Pade'
     approximation when 'Dq(A)' is ill-conditioned.

     See also: *note logm: XREFlogm, *note sqrtm: XREFsqrtm.

 -- Function File: S = logm (A)
 -- Function File: S = logm (A, OPT_ITERS)
 -- Function File: [S, ITERS] = logm (...)
     Compute the matrix logarithm of the square matrix A.  The
     implementation utilizes a Pade' approximant and the identity

          logm (A) = 2^k * logm (A^(1 / 2^k))

     The optional argument OPT_ITERS is the maximum number of square
     roots to compute and defaults to 100.  The optional output ITERS is
     the number of square roots actually computed.

     See also: *note expm: XREFexpm, *note sqrtm: XREFsqrtm.

 -- Built-in Function: S = sqrtm (A)
 -- Built-in Function: [S, ERROR_ESTIMATE] = sqrtm (A)
     Compute the matrix square root of the square matrix A.

     Ref: N.J. Higham.  'A New sqrtm for MATLAB'.  Numerical Analysis
     Report No.  336, Manchester Centre for Computational Mathematics,
     Manchester, England, January 1999.

     See also: *note expm: XREFexpm, *note logm: XREFlogm.

 -- Built-in Function: kron (A, B)
 -- Built-in Function: kron (A1, A2, ...)
     Form the Kronecker product of two or more matrices, defined block
     by block as

          x = [ a(i,j)*b ]

     For example:

          kron (1:4, ones (3, 1))
               =>  1  2  3  4
                   1  2  3  4
                   1  2  3  4

     If there are more than two input arguments A1, A2, ..., AN the
     Kronecker product is computed as

          kron (kron (A1, A2), ..., AN)

     Since the Kronecker product is associative, this is well-defined.

 -- Built-in Function: blkmm (A, B)
     Compute products of matrix blocks.  The blocks are given as
     2-dimensional subarrays of the arrays A, B.  The size of A must
     have the form '[m,k,...]' and size of B must be '[k,n,...]'.  The
     result is then of size '[m,n,...]' and is computed as follows:

          for i = 1:prod (size (A)(3:end))
            C(:,:,i) = A(:,:,i) * B(:,:,i)
          endfor

 -- Built-in Function: X = syl (A, B, C)
     Solve the Sylvester equation

          A X + X B = C

     using standard LAPACK subroutines.

     For example:

          sylvester ([1, 2; 3, 4], [5, 6; 7, 8], [9, 10; 11, 12])
             => [ 0.50000, 0.66667; 0.66667, 0.50000 ]


File: octave.info,  Node: Specialized Solvers,  Prev: Functions of a Matrix,  Up: Linear Algebra

18.5 Specialized Solvers
========================

 -- Function File: X = bicg (A, B, RTOL, MAXIT, M1, M2, X0)
 -- Function File: X = bicg (A, B, RTOL, MAXIT, P)
 -- Function File: [X, FLAG, RELRES, ITER, RESVEC] = bicg (A, B, ...)
     Solve 'A x = b' using the Bi-conjugate gradient iterative method.

        - RTOL is the relative tolerance, if not given or set to [] the
          default value 1e-6 is used.

        - MAXIT the maximum number of outer iterations, if not given or
          set to [] the default value 'min (20, numel (b))' is used.

        - X0 the initial guess, if not given or set to [] the default
          value 'zeros (size (b))' is used.

     A can be passed as a matrix or as a function handle or inline
     function 'f' such that 'f(x, "notransp") = A*x' and 'f(x, "transp")
     = A'*x'.

     The preconditioner P is given as 'P = M1 * M2'.  Both M1 and M2 can
     be passed as a matrix or as a function handle or inline function
     'g' such that 'g(x, "notransp") = M1 \ x' or 'g(x, "notransp") = M2
     \ x' and 'g(x, "transp") = M1' \ x' or 'g(x, "transp") = M2' \ x'.

     If called with more than one output parameter

        - FLAG indicates the exit status:

             - 0: iteration converged to the within the chosen tolerance

             - 1: the maximum number of iterations was reached before
               convergence

             - 3: the algorithm reached stagnation

          (the value 2 is unused but skipped for compatibility).

        - RELRES is the final value of the relative residual.

        - ITER is the number of iterations performed.

        - RESVEC is a vector containing the relative residual at each
          iteration.

     See also: *note bicgstab: XREFbicgstab, *note cgs: XREFcgs, *note
     gmres: XREFgmres, *note pcg: XREFpcg, *note qmr: XREFqmr.

 -- Function File: X = bicgstab (A, B, RTOL, MAXIT, M1, M2, X0)
 -- Function File: X = bicgstab (A, B, RTOL, MAXIT, P)
 -- Function File: [X, FLAG, RELRES, ITER, RESVEC] = bicgstab (A, B,
          ...)
     Solve 'A x = b' using the stabilizied Bi-conjugate gradient
     iterative method.

        - RTOL is the relative tolerance, if not given or set to [] the
          default value 1e-6 is used.

        - MAXIT the maximum number of outer iterations, if not given or
          set to [] the default value 'min (20, numel (b))' is used.

        - X0 the initial guess, if not given or set to [] the default
          value 'zeros (size (b))' is used.

     A can be passed as a matrix or as a function handle or inline
     function 'f' such that 'f(x) = A*x'.

     The preconditioner P is given as 'P = M1 * M2'.  Both M1 and M2 can
     be passed as a matrix or as a function handle or inline function
     'g' such that 'g(x) = M1 \ x' or 'g(x) = M2 \ x'.

     If called with more than one output parameter

        - FLAG indicates the exit status:

             - 0: iteration converged to the within the chosen tolerance

             - 1: the maximum number of iterations was reached before
               convergence

             - 3: the algorithm reached stagnation

          (the value 2 is unused but skipped for compatibility).

        - RELRES is the final value of the relative residual.

        - ITER is the number of iterations performed.

        - RESVEC is a vector containing the relative residual at each
          iteration.

     See also: *note bicg: XREFbicg, *note cgs: XREFcgs, *note gmres:
     XREFgmres, *note pcg: XREFpcg, *note qmr: XREFqmr.

 -- Function File: X = cgs (A, B, RTOL, MAXIT, M1, M2, X0)
 -- Function File: X = cgs (A, B, RTOL, MAXIT, P)
 -- Function File: [X, FLAG, RELRES, ITER, RESVEC] = cgs (A, B, ...)
     Solve 'A x = b', where A is a square matrix, using the Conjugate
     Gradients Squared method.

        - RTOL is the relative tolerance, if not given or set to [] the
          default value 1e-6 is used.

        - MAXIT the maximum number of outer iterations, if not given or
          set to [] the default value 'min (20, numel (b))' is used.

        - X0 the initial guess, if not given or set to [] the default
          value 'zeros (size (b))' is used.

     A can be passed as a matrix or as a function handle or inline
     function 'f' such that 'f(x) = A*x'.

     The preconditioner P is given as 'P = M1 * M2'.  Both M1 and M2 can
     be passed as a matrix or as a function handle or inline function
     'g' such that 'g(x) = M1 \ x' or 'g(x) = M2 \ x'.

     If called with more than one output parameter

        - FLAG indicates the exit status:

             - 0: iteration converged to the within the chosen tolerance

             - 1: the maximum number of iterations was reached before
               convergence

             - 3: the algorithm reached stagnation

          (the value 2 is unused but skipped for compatibility).

        - RELRES is the final value of the relative residual.

        - ITER is the number of iterations performed.

        - RESVEC is a vector containing the relative residual at each
          iteration.

     See also: *note pcg: XREFpcg, *note bicgstab: XREFbicgstab, *note
     bicg: XREFbicg, *note gmres: XREFgmres, *note qmr: XREFqmr.

 -- Function File: X = gmres (A, B, M, RTOL, MAXIT, M1, M2, X0)
 -- Function File: X = gmres (A, B, M, RTOL, MAXIT, P)
 -- Function File: [X, FLAG, RELRES, ITER, RESVEC] = gmres (...)
     Solve 'A x = b' using the Preconditioned GMRES iterative method
     with restart, a.k.a.  PGMRES(m).

        - RTOL is the relative tolerance, if not given or set to [] the
          default value 1e-6 is used.

        - MAXIT is the maximum number of outer iterations, if not given
          or set to [] the default value 'min (10, numel (b) / restart)'
          is used.

        - X0 is the initial guess, if not given or set to [] the default
          value 'zeros (size (b))' is used.

        - M is the restart parameter, if not given or set to [] the
          default value 'numel (b)' is used.

     Argument A can be passed as a matrix, function handle, or inline
     function 'f' such that 'f(x) = A*x'.

     The preconditioner P is given as 'P = M1 * M2'.  Both M1 and M2 can
     be passed as a matrix, function handle, or inline function 'g' such
     that 'g(x) = M1\x' or 'g(x) = M2\x'.

     Besides the vector X, additional outputs are:

        - FLAG indicates the exit status:

          0 : iteration converged to within the specified tolerance

          1 : maximum number of iterations exceeded

          2 : unused, but skipped for compatibility

          3 : algorithm reached stagnation (no change between iterations)

        - RELRES is the final value of the relative residual.

        - ITER is a vector containing the number of outer iterations and
          total iterations performed.

        - RESVEC is a vector containing the relative residual at each
          iteration.

     See also: *note bicg: XREFbicg, *note bicgstab: XREFbicgstab, *note
     cgs: XREFcgs, *note pcg: XREFpcg, *note pcr: XREFpcr, *note qmr:
     XREFqmr.

 -- Function File: X = qmr (A, B, RTOL, MAXIT, M1, M2, X0)
 -- Function File: X = qmr (A, B, RTOL, MAXIT, P)
 -- Function File: [X, FLAG, RELRES, ITER, RESVEC] = qmr (A, B, ...)
     Solve 'A x = b' using the Quasi-Minimal Residual iterative method
     (without look-ahead).

        - RTOL is the relative tolerance, if not given or set to [] the
          default value 1e-6 is used.

        - MAXIT the maximum number of outer iterations, if not given or
          set to [] the default value 'min (20, numel (b))' is used.

        - X0 the initial guess, if not given or set to [] the default
          value 'zeros (size (b))' is used.

     A can be passed as a matrix or as a function handle or inline
     function 'f' such that 'f(x, "notransp") = A*x' and 'f(x, "transp")
     = A'*x'.

     The preconditioner P is given as 'P = M1 * M2'.  Both M1 and M2 can
     be passed as a matrix or as a function handle or inline function
     'g' such that 'g(x, "notransp") = M1 \ x' or 'g(x, "notransp") = M2
     \ x' and 'g(x, "transp") = M1' \ x' or 'g(x, "transp") = M2' \ x'.

     If called with more than one output parameter

        - FLAG indicates the exit status:

             - 0: iteration converged to the within the chosen tolerance

             - 1: the maximum number of iterations was reached before
               convergence

             - 3: the algorithm reached stagnation

          (the value 2 is unused but skipped for compatibility).

        - RELRES is the final value of the relative residual.

        - ITER is the number of iterations performed.

        - RESVEC is a vector containing the residual norms at each
          iteration.

     References:

       1. R. Freund and N. Nachtigal, 'QMR: a quasi-minimal residual
          method for non-Hermitian linear systems', Numerische
          Mathematik, 1991, 60, pp.  315-339.

       2. R. Barrett, M. Berry, T. Chan, J. Demmel, J. Donato, J.
          Dongarra, V. Eijkhour, R. Pozo, C. Romine, and H. van der
          Vorst, 'Templates for the solution of linear systems: Building
          blocks for iterative methods', SIAM, 2nd ed., 1994.

     See also: *note bicg: XREFbicg, *note bicgstab: XREFbicgstab, *note
     cgs: XREFcgs, *note gmres: XREFgmres, *note pcg: XREFpcg.


File: octave.info,  Node: Vectorization and Faster Code Execution,  Next: Nonlinear Equations,  Prev: Linear Algebra,  Up: Top

19 Vectorization and Faster Code Execution
******************************************

Vectorization is a programming technique that uses vector operations
instead of element-by-element loop-based operations.  Besides frequently
producing more succinct Octave code, vectorization also allows for
better optimization in the subsequent implementation.  The optimizations
may occur either in Octave's own Fortran, C, or C++ internal
implementation, or even at a lower level depending on the compiler and
external numerical libraries used to build Octave.  The ultimate goal is
to make use of your hardware's vector instructions if possible or to
perform other optimizations in software.

   Vectorization is not a concept unique to Octave, but it is
particularly important because Octave is a matrix-oriented language.
Vectorized Octave code will see a dramatic speed up (10X-100X) in most
cases.

   This chapter discusses vectorization and other techniques for writing
faster code.

* Menu:

* Basic Vectorization::        Basic techniques for code optimization
* Broadcasting::               Broadcasting operations
* Function Application::       Applying functions to arrays, cells, and structs
* Accumulation::               Accumulation functions
* JIT Compiler::               Just-In-Time Compiler for loops
* Miscellaneous Techniques::   Other techniques for speeding up code
* Examples::


File: octave.info,  Node: Basic Vectorization,  Next: Broadcasting,  Up: Vectorization and Faster Code Execution

19.1 Basic Vectorization
========================

To a very good first approximation, the goal in vectorization is to
write code that avoids loops and uses whole-array operations.  As a
trivial example, consider

     for i = 1:n
       for j = 1:m
         c(i,j) = a(i,j) + b(i,j);
       endfor
     endfor

compared to the much simpler

     c = a + b;

This isn't merely easier to write; it is also internally much easier to
optimize.  Octave delegates this operation to an underlying
implementation which, among other optimizations, may use special vector
hardware instructions or could conceivably even perform the additions in
parallel.  In general, if the code is vectorized, the underlying
implementation has more freedom about the assumptions it can make in
order to achieve faster execution.

   This is especially important for loops with "cheap" bodies.  Often it
suffices to vectorize just the innermost loop to get acceptable
performance.  A general rule of thumb is that the "order" of the
vectorized body should be greater or equal to the "order" of the
enclosing loop.

   As a less trivial example, instead of

     for i = 1:n-1
       a(i) = b(i+1) - b(i);
     endfor

write

     a = b(2:n) - b(1:n-1);

   This shows an important general concept about using arrays for
indexing instead of looping over an index variable.  *Note Index
Expressions::.  Also use boolean indexing generously.  If a condition
needs to be tested, this condition can also be written as a boolean
index.  For instance, instead of

     for i = 1:n
       if (a(i) > 5)
         a(i) -= 20
       endif
     endfor

write

     a(a>5) -= 20;

which exploits the fact that 'a > 5' produces a boolean index.

   Use elementwise vector operators whenever possible to avoid looping
(operators like '.*' and '.^').  *Note Arithmetic Ops::.  For simple
inline functions, the 'vectorize' function can do this automatically.

 -- Built-in Function: vectorize (FUN)
     Create a vectorized version of the inline function FUN by replacing
     all occurrences of '*', '/', etc., with '.*', './', etc.

     This may be useful, for example, when using inline functions with
     numerical integration or optimization where a vector-valued
     function is expected.

          fcn = vectorize (inline ("x^2 - 1"))
             => fcn = f(x) = x.^2 - 1
          quadv (fcn, 0, 3)
             => 6

     See also: *note inline: XREFinline, *note formula: XREFformula,
     *note argnames: XREFargnames.

   Also exploit broadcasting in these elementwise operators both to
avoid looping and unnecessary intermediate memory allocations.  *Note
Broadcasting::.

   Use built-in and library functions if possible.  Built-in and
compiled functions are very fast.  Even with an m-file library function,
chances are good that it is already optimized, or will be optimized more
in a future release.

   For instance, even better than

     a = b(2:n) - b(1:n-1);

is

     a = diff (b);

   Most Octave functions are written with vector and array arguments in
mind.  If you find yourself writing a loop with a very simple operation,
chances are that such a function already exists.  The following
functions occur frequently in vectorized code:

   * Index manipulation

        * find

        * sub2ind

        * ind2sub

        * sort

        * unique

        * lookup

        * ifelse / merge

   * Repetition

        * repmat

        * repelems

   * Vectorized arithmetic

        * sum

        * prod

        * cumsum

        * cumprod

        * sumsq

        * diff

        * dot

        * cummax

        * cummin

   * Shape of higher dimensional arrays

        * reshape

        * resize

        * permute

        * squeeze

        * deal


File: octave.info,  Node: Broadcasting,  Next: Function Application,  Prev: Basic Vectorization,  Up: Vectorization and Faster Code Execution

19.2 Broadcasting
=================

Broadcasting refers to how Octave binary operators and functions behave
when their matrix or array operands or arguments differ in size.  Since
version 3.6.0, Octave now automatically broadcasts vectors, matrices,
and arrays when using elementwise binary operators and functions.
Broadly speaking, smaller arrays are "broadcast" across the larger one,
until they have a compatible shape.  The rule is that corresponding
array dimensions must either

  1. be equal, or

  2. one of them must be 1.

In case all dimensions are equal, no broadcasting occurs and ordinary
element-by-element arithmetic takes place.  For arrays of higher
dimensions, if the number of dimensions isn't the same, then missing
trailing dimensions are treated as 1.  When one of the dimensions is 1,
the array with that singleton dimension gets copied along that dimension
until it matches the dimension of the other array.  For example,
consider

     x = [1 2 3;
          4 5 6;
          7 8 9];

     y = [10 20 30];

     x + y

Without broadcasting, 'x + y' would be an error because the dimensions
do not agree.  However, with broadcasting it is as if the following
operation were performed:

     x = [1 2 3
          4 5 6
          7 8 9];

     y = [10 20 30
          10 20 30
          10 20 30];

     x + y
     =>    11   22   33
           14   25   36
           17   28   39

That is, the smaller array of size '[1 3]' gets copied along the
singleton dimension (the number of rows) until it is '[3 3]'.  No actual
copying takes place, however.  The internal implementation reuses
elements along the necessary dimension in order to achieve the desired
effect without copying in memory.

   Both arrays can be broadcast across each other, for example, all
pairwise differences of the elements of a vector with itself:

     y - y'
     =>    0   10   20
         -10    0   10
         -20  -10    0

Here the vectors of size '[1 3]' and '[3 1]' both get broadcast into
matrices of size '[3 3]' before ordinary matrix subtraction takes place.

   A special case of broadcasting that may be familiar is when all
dimensions of the array being broadcast are 1, i.e., the array is a
scalar.  Thus for example, operations like 'x - 42' and 'max (x, 2)' are
basic examples of broadcasting.

   For a higher-dimensional example, suppose 'img' is an RGB image of
size '[m n 3]' and we wish to multiply each color by a different scalar.
The following code accomplishes this with broadcasting,

     img .*= permute ([0.8, 0.9, 1.2], [1, 3, 2]);

Note the usage of permute to match the dimensions of the '[0.8, 0.9,
1.2]' vector with 'img'.

   For functions that are not written with broadcasting semantics,
'bsxfun' can be useful for coercing them to broadcast.

 -- Built-in Function: bsxfun (F, A, B)
     The binary singleton expansion function applier performs
     broadcasting, that is, applies a binary function F
     element-by-element to two array arguments A and B, and expands as
     necessary singleton dimensions in either input argument.  F is a
     function handle, inline function, or string containing the name of
     the function to evaluate.  The function F must be capable of
     accepting two column-vector arguments of equal length, or one
     column vector argument and a scalar.

     The dimensions of A and B must be equal or singleton.  The
     singleton dimensions of the arrays will be expanded to the same
     dimensionality as the other array.

     See also: *note arrayfun: XREFarrayfun, *note cellfun: XREFcellfun.

   Broadcasting is only applied if either of the two broadcasting
conditions hold.  As usual, however, broadcasting does not apply when
two dimensions differ and neither is 1:

     x = [1 2 3
          4 5 6];
     y = [10 20
          30 40];
     x + y

This will produce an error about nonconformant arguments.

   Besides common arithmetic operations, several functions of two
arguments also broadcast.  The full list of functions and operators that
broadcast is

           plus      +  .+
           minus     -  .-
           times     .*
           rdivide   ./
           ldivide   .\
           power     .^  .**
           lt        <
           le        <=
           eq        ==
           gt        >
           ge        >=
           ne        !=  ~=
           and       &
           or        |
           atan2
           hypot
           max
           min
           mod
           rem
           xor

           +=  -=  .+=  .-=  .*=  ./=  .\=  .^=  .**=  &=  |=

   Beware of resorting to broadcasting if a simpler operation will
suffice.  For matrices A and B, consider the following:

     C = sum (permute (A, [1, 3, 2]) .* permute (B, [3, 2, 1]), 3);

This operation broadcasts the two matrices with permuted dimensions
across each other during elementwise multiplication in order to obtain a
larger 3-D array, and this array is then summed along the third
dimension.  A moment of thought will prove that this operation is simply
the much faster ordinary matrix multiplication, 'C = A*B;'.

   A note on terminology: "broadcasting" is the term popularized by the
Numpy numerical environment in the Python programming language.  In
other programming languages and environments, broadcasting may also be
known as _binary singleton expansion_ (BSX, in MATLAB, and the origin of
the name of the 'bsxfun' function), _recycling_ (R programming
language), _single-instruction multiple data_ (SIMD), or _replication_.

19.2.1 Broadcasting and Legacy Code
-----------------------------------

The new broadcasting semantics almost never affect code that worked in
previous versions of Octave.  Consequently, all code inherited from
MATLAB that worked in previous versions of Octave should still work
without change in Octave.  The only exception is code such as

     try
       c = a.*b;
     catch
       c = a.*a;
     end_try_catch

that may have relied on matrices of different size producing an error.
Due to how broadcasting changes semantics with older versions of Octave,
by default Octave warns if a broadcasting operation is performed.  To
disable this warning, refer to its ID (*note warning_ids:
XREFwarning_ids.):

     warning ("off", "Octave:broadcast");

If you want to recover the old behavior and produce an error, turn this
warning into an error:

     warning ("error", "Octave:broadcast");

For broadcasting on scalars that worked in previous versions of Octave,
this warning will not be emitted.


File: octave.info,  Node: Function Application,  Next: Accumulation,  Prev: Broadcasting,  Up: Vectorization and Faster Code Execution

19.3 Function Application
=========================

As a general rule, functions should already be written with matrix
arguments in mind and should consider whole matrix operations in a
vectorized manner.  Sometimes, writing functions in this way appears
difficult or impossible for various reasons.  For those situations,
Octave provides facilities for applying a function to each element of an
array, cell, or struct.

 -- Function File: arrayfun (FUNC, A)
 -- Function File: X = arrayfun (FUNC, A)
 -- Function File: X = arrayfun (FUNC, A, B, ...)
 -- Function File: [X, Y, ...] = arrayfun (FUNC, A, ...)
 -- Function File: arrayfun (..., "UniformOutput", VAL)
 -- Function File: arrayfun (..., "ErrorHandler", ERRFUNC)

     Execute a function on each element of an array.  This is useful for
     functions that do not accept array arguments.  If the function does
     accept array arguments it is better to call the function directly.

     The first input argument FUNC can be a string, a function handle,
     an inline function, or an anonymous function.  The input argument A
     can be a logic array, a numeric array, a string array, a structure
     array, or a cell array.  By a call of the function 'arrayfun' all
     elements of A are passed on to the named function FUNC
     individually.

     The named function can also take more than two input arguments,
     with the input arguments given as third input argument B, fourth
     input argument C, ... If given more than one array input argument
     then all input arguments must have the same sizes, for example:

          arrayfun (@atan2, [1, 0], [0, 1])
               => [ 1.5708   0.0000 ]

     If the parameter VAL after a further string input argument
     "UniformOutput" is set 'true' (the default), then the named
     function FUNC must return a single element which then will be
     concatenated into the return value and is of type matrix.
     Otherwise, if that parameter is set to 'false', then the outputs
     are concatenated in a cell array.  For example:

          arrayfun (@(x,y) x:y, "abc", "def", "UniformOutput", false)
          =>
             {
               [1,1] = abcd
               [1,2] = bcde
               [1,3] = cdef
             }

     If more than one output arguments are given then the named function
     must return the number of return values that also are expected, for
     example:

          [A, B, C] = arrayfun (@find, [10; 0], "UniformOutput", false)
          =>
          A =
          {
             [1,1] =  1
             [2,1] = [](0x0)
          }
          B =
          {
             [1,1] =  1
             [2,1] = [](0x0)
          }
          C =
          {
             [1,1] =  10
             [2,1] = [](0x0)
          }

     If the parameter ERRFUNC after a further string input argument
     "ErrorHandler" is another string, a function handle, an inline
     function, or an anonymous function, then ERRFUNC defines a function
     to call in the case that FUNC generates an error.  The definition
     of the function must be of the form

          function [...] = errfunc (S, ...)

     where there is an additional input argument to ERRFUNC relative to
     FUNC, given by S.  This is a structure with the elements
     "identifier", "message", and "index" giving, respectively, the
     error identifier, the error message, and the index of the array
     elements that caused the error.  The size of the output argument of
     ERRFUNC must have the same size as the output argument of FUNC,
     otherwise a real error is thrown.  For example:

          function y = ferr (s, x), y = "MyString"; endfunction
          arrayfun (@str2num, [1234],
                    "UniformOutput", false, "ErrorHandler", @ferr)
          =>
             {
               [1,1] = MyString
             }

     See also: *note spfun: XREFspfun, *note cellfun: XREFcellfun, *note
     structfun: XREFstructfun.

 -- Function File: Y = spfun (F, S)
     Compute 'f(S)' for the nonzero values of S.  This results in a
     sparse matrix with the same structure as S.  The function F can be
     passed as a string, a function handle, or an inline function.

     See also: *note arrayfun: XREFarrayfun, *note cellfun: XREFcellfun,
     *note structfun: XREFstructfun.

 -- Built-in Function: cellfun (NAME, C)
 -- Built-in Function: cellfun ("size", C, K)
 -- Built-in Function: cellfun ("isclass", C, CLASS)
 -- Built-in Function: cellfun (FUNC, C)
 -- Built-in Function: cellfun (FUNC, C, D)
 -- Built-in Function: [A, ...] = cellfun (...)
 -- Built-in Function: cellfun (..., "ErrorHandler", ERRFUNC)
 -- Built-in Function: cellfun (..., "UniformOutput", VAL)

     Evaluate the function named NAME on the elements of the cell array
     C.  Elements in C are passed on to the named function individually.
     The function NAME can be one of the functions

     'isempty'
          Return 1 for empty elements.

     'islogical'
          Return 1 for logical elements.

     'isnumeric'
          Return 1 for numeric elements.

     'isreal'
          Return 1 for real elements.

     'length'
          Return a vector of the lengths of cell elements.

     'ndims'
          Return the number of dimensions of each element.

     'numel'
     'prodofsize'
          Return the number of elements contained within each cell
          element.  The number is the product of the dimensions of the
          object at each cell element.

     'size'
          Return the size along the K-th dimension.

     'isclass'
          Return 1 for elements of CLASS.

     Additionally, 'cellfun' accepts an arbitrary function FUNC in the
     form of an inline function, function handle, or the name of a
     function (in a character string).  The function can take one or
     more arguments, with the inputs arguments given by C, D, etc.
     Equally the function can return one or more output arguments.  For
     example:

          cellfun ("atan2", {1, 0}, {0, 1})
               => [ 1.57080   0.00000 ]

     The number of output arguments of 'cellfun' matches the number of
     output arguments of the function.  The outputs of the function will
     be collected into the output arguments of 'cellfun' like this:

          function [a, b] = twoouts (x)
            a = x;
            b = x*x;
          endfunction
          [aa, bb] = cellfun (@twoouts, {1, 2, 3})
               =>
                  aa =
                     1 2 3
                  bb =
                     1 4 9

     Note that per default the output argument(s) are arrays of the same
     size as the input arguments.  Input arguments that are singleton
     (1x1) cells will be automatically expanded to the size of the other
     arguments.

     If the parameter "UniformOutput" is set to true (the default), then
     the function must return scalars which will be concatenated into
     the return array(s).  If "UniformOutput" is false, the outputs are
     concatenated into a cell array (or cell arrays).  For example:

          cellfun ("tolower", {"Foo", "Bar", "FooBar"},
                   "UniformOutput", false)
          => {"foo", "bar", "foobar"}

     Given the parameter "ErrorHandler", then ERRFUNC defines a function
     to call in case FUNC generates an error.  The form of the function
     is

          function [...] = errfunc (S, ...)

     where there is an additional input argument to ERRFUNC relative to
     FUNC, given by S.  This is a structure with the elements
     "identifier", "message" and "index", giving respectively the error
     identifier, the error message, and the index into the input
     arguments of the element that caused the error.  For example:

          function y = foo (s, x), y = NaN; endfunction
          cellfun ("factorial", {-1,2}, "ErrorHandler", @foo)
          => [NaN 2]

     Use 'cellfun' intelligently.  The 'cellfun' function is a useful
     tool for avoiding loops.  It is often used with anonymous function
     handles; however, calling an anonymous function involves an
     overhead quite comparable to the overhead of an m-file function.
     Passing a handle to a built-in function is faster, because the
     interpreter is not involved in the internal loop.  For example:

          a = {...}
          v = cellfun (@(x) det (x), a); # compute determinants
          v = cellfun (@det, a); # faster

     See also: *note arrayfun: XREFarrayfun, *note structfun:
     XREFstructfun, *note spfun: XREFspfun.

 -- Function File: structfun (FUNC, S)
 -- Function File: [A, ...] = structfun (...)
 -- Function File: structfun (..., "ErrorHandler", ERRFUNC)
 -- Function File: structfun (..., "UniformOutput", VAL)

     Evaluate the function named NAME on the fields of the structure S.
     The fields of S are passed to the function FUNC individually.

     'structfun' accepts an arbitrary function FUNC in the form of an
     inline function, function handle, or the name of a function (in a
     character string).  In the case of a character string argument, the
     function must accept a single argument named X, and it must return
     a string value.  If the function returns more than one argument,
     they are returned as separate output variables.

     If the parameter "UniformOutput" is set to true (the default), then
     the function must return a single element which will be
     concatenated into the return value.  If "UniformOutput" is false,
     the outputs are placed into a structure with the same fieldnames as
     the input structure.

          s.name1 = "John Smith";
          s.name2 = "Jill Jones";
          structfun (@(x) regexp (x, '(\w+)$', "matches"){1}, s,
                     "UniformOutput", false)
          =>
             {
               name1 = Smith
               name2 = Jones
             }

     Given the parameter "ErrorHandler", ERRFUNC defines a function to
     call in case FUNC generates an error.  The form of the function is

          function [...] = errfunc (SE, ...)

     where there is an additional input argument to ERRFUNC relative to
     FUNC, given by SE.  This is a structure with the elements
     "identifier", "message" and "index", giving respectively the error
     identifier, the error message, and the index into the input
     arguments of the element that caused the error.  For an example on
     how to use an error handler, *note cellfun: XREFcellfun.

     See also: *note cellfun: XREFcellfun, *note arrayfun: XREFarrayfun,
     *note spfun: XREFspfun.

   Consistent with earlier advice, seek to use Octave built-in functions
whenever possible for the best performance.  This advice applies
especially to the four functions above.  For example, when adding two
arrays together element-by-element one could use a handle to the
built-in addition function '@plus' or define an anonymous function
'@(x,y) x + y'.  But, the anonymous function is 60% slower than the
first method.  *Note Operator Overloading::, for a list of basic
functions which might be used in place of anonymous ones.


File: octave.info,  Node: Accumulation,  Next: JIT Compiler,  Prev: Function Application,  Up: Vectorization and Faster Code Execution

19.4 Accumulation
=================

Whenever it's possible to categorize according to indices the elements
of an array when performing a computation, accumulation functions can be
useful.

 -- Function File: accumarray (SUBS, VALS, SZ, FUNC, FILLVAL, ISSPARSE)
 -- Function File: accumarray (SUBS, VALS, ...)

     Create an array by accumulating the elements of a vector into the
     positions defined by their subscripts.  The subscripts are defined
     by the rows of the matrix SUBS and the values by VALS.  Each row of
     SUBS corresponds to one of the values in VALS.  If VALS is a
     scalar, it will be used for each of the row of SUBS.  If SUBS is a
     cell array of vectors, all vectors must be of the same length, and
     the subscripts in the Kth vector must correspond to the Kth
     dimension of the result.

     The size of the matrix will be determined by the subscripts
     themselves.  However, if SZ is defined it determines the matrix
     size.  The length of SZ must correspond to the number of columns in
     SUBS.  An exception is if SUBS has only one column, in which case
     SZ may be the dimensions of a vector and the subscripts of SUBS are
     taken as the indices into it.

     The default action of 'accumarray' is to sum the elements with the
     same subscripts.  This behavior can be modified by defining the
     FUNC function.  This should be a function or function handle that
     accepts a column vector and returns a scalar.  The result of the
     function should not depend on the order of the subscripts.

     The elements of the returned array that have no subscripts
     associated with them are set to zero.  Defining FILLVAL to some
     other value allows these values to be defined.  This behavior
     changes, however, for certain values of FUNC.  If FUNC is 'min'
     (respectively, 'max') then the result will be filled with the
     minimum (respectively, maximum) integer if VALS is of integral
     type, logical false (respectively, logical true) if VALS is of
     logical type, zero if FILLVAL is zero and all values are
     non-positive (respectively, non-negative), and NaN otherwise.

     By default 'accumarray' returns a full matrix.  If ISSPARSE is
     logically true, then a sparse matrix is returned instead.

     The following 'accumarray' example constructs a frequency table
     that in the first column counts how many occurrences each number in
     the second column has, taken from the vector X.  Note the usage of
     'unique' for assigning to all repeated elements of X the same index
     (*note unique: XREFunique.).

          X = [91, 92, 90, 92, 90, 89, 91, 89, 90, 100, 100, 100];
          [U, ~, J] = unique (X);
          [accumarray(J', 1), U']
            =>  2    89
                3    90
                2    91
                2    92
                3   100

     Another example, where the result is a multi-dimensional 3-D array
     and the default value (zero) appears in the output:

          accumarray ([1, 1, 1;
                       2, 1, 2;
                       2, 3, 2;
                       2, 1, 2;
                       2, 3, 2], 101:105)
          => ans(:,:,1) = [101, 0, 0; 0, 0, 0]
          => ans(:,:,2) = [0, 0, 0; 206, 0, 208]

     The sparse option can be used as an alternative to the 'sparse'
     constructor (*note sparse: XREFsparse.).  Thus

          sparse (I, J, SV)

     can be written with 'accumarray' as

          accumarray ([I, J], SV', [], [], 0, true)

     For repeated indices, 'sparse' adds the corresponding value.  To
     take the minimum instead, use 'min' as an accumulator function:

          accumarray ([I, J], SV', [], @min, 0, true)

     The complexity of accumarray in general for the non-sparse case is
     generally O(M+N), where N is the number of subscripts and M is the
     maximum subscript (linearized in multi-dimensional case).  If FUNC
     is one of '@sum' (default), '@max', '@min' or '@(x) {x}', an
     optimized code path is used.  Note that for general reduction
     function the interpreter overhead can play a major part and it may
     be more efficient to do multiple accumarray calls and compute the
     results in a vectorized manner.

     See also: *note accumdim: XREFaccumdim, *note unique: XREFunique,
     *note sparse: XREFsparse.

 -- Function File: accumdim (SUBS, VALS, DIM, N, FUNC, FILLVAL)
     Create an array by accumulating the slices of an array into the
     positions defined by their subscripts along a specified dimension.
     The subscripts are defined by the index vector SUBS.  The dimension
     is specified by DIM.  If not given, it defaults to the first
     non-singleton dimension.  The length of SUBS must be equal to 'size
     (VALS, DIM)'.

     The extent of the result matrix in the working dimension will be
     determined by the subscripts themselves.  However, if N is defined
     it determines this extent.

     The default action of 'accumdim' is to sum the subarrays with the
     same subscripts.  This behavior can be modified by defining the
     FUNC function.  This should be a function or function handle that
     accepts an array and a dimension, and reduces the array along this
     dimension.  As a special exception, the built-in 'min' and 'max'
     functions can be used directly, and 'accumdim' accounts for the
     middle empty argument that is used in their calling.

     The slices of the returned array that have no subscripts associated
     with them are set to zero.  Defining FILLVAL to some other value
     allows these values to be defined.

     An example of the use of 'accumdim' is:

          accumdim ([1, 2, 1, 2, 1], [ 7, -10,   4;
                                      -5, -12,   8;
                                     -12,   2,   8;
                                     -10,   9,  -3;
                                      -5,  -3, -13])
          => [-10,-11,-1;-15,-3,5]

     See also: *note accumarray: XREFaccumarray.


File: octave.info,  Node: JIT Compiler,  Next: Miscellaneous Techniques,  Prev: Accumulation,  Up: Vectorization and Faster Code Execution

19.5 JIT Compiler
=================

Vectorization is the preferred technique for eliminating loops and
speeding up code.  Nevertheless, it is not always possible to replace
every loop.  In such situations it may be worth trying Octave's
*experimental* Just-In-Time (JIT) compiler.

   A JIT compiler works by analyzing the body of a loop, translating the
Octave statements into another language, compiling the new code segment
into an executable, and then running the executable and collecting any
results.  The process is not simple and there is a significant amount of
work to perform for each step.  It can still make sense, however, if the
number of loop iterations is large.  Because Octave is an interpreted
language every time through a loop Octave must parse the statements in
the loop body before executing them.  With a JIT compiler this is done
just once when the body is translated to another language.

   The JIT compiler is a very new feature in Octave and not all valid
Octave statements can currently be accelerated.  However, if no other
technique is available it may be worth benchmarking the code with JIT
enabled.  The function 'jit_enable' is used to turn compilation on or
off.  The function 'jit_startcnt' sets the threshold for acceleration.
Loops with iteration counts above 'jit_startcnt' will be accelerated.
The functions 'jit_failcnt' and 'debug_jit' are not likely to be of use
to anyone not working directly on the implementation of the JIT
compiler.

 -- Built-in Function: VAL = jit_enable ()
 -- Built-in Function: OLD_VAL = jit_enable (NEW_VAL)
 -- Built-in Function: jit_enable (NEW_VAL, "local")
     Query or set the internal variable that enables Octave's JIT
     compiler.

     When called from inside a function with the "local" option, the
     variable is changed locally for the function and any subroutines it
     calls.  The original variable value is restored when exiting the
     function.

     See also: *note jit_startcnt: XREFjit_startcnt, *note debug_jit:
     XREFdebug_jit.

 -- Built-in Function: VAL = jit_startcnt ()
 -- Built-in Function: OLD_VAL = jit_startcnt (NEW_VAL)
 -- Built-in Function: jit_startcnt (NEW_VAL, "local")
     Query or set the internal variable that determines whether JIT
     compilation will take place for a specific loop.  Because
     compilation is a costly operation it does not make sense to employ
     JIT when the loop count is low.  By default only loops with greater
     than 1000 iterations will be accelerated.

     When called from inside a function with the "local" option, the
     variable is changed locally for the function and any subroutines it
     calls.  The original variable value is restored when exiting the
     function.

     See also: *note jit_enable: XREFjit_enable, *note debug_jit:
     XREFdebug_jit.

 -- Built-in Function: VAL = jit_failcnt ()
 -- Built-in Function: OLD_VAL = jit_failcnt (NEW_VAL)
 -- Built-in Function: jit_failcnt (NEW_VAL, "local")
     Query or set the internal variable that counts the number of JIT
     fail exceptions for Octave's JIT compiler.

     When called from inside a function with the "local" option, the
     variable is changed locally for the function and any subroutines it
     calls.  The original variable value is restored when exiting the
     function.

     See also: *note jit_enable: XREFjit_enable, *note jit_startcnt:
     XREFjit_startcnt, *note debug_jit: XREFdebug_jit.

 -- Built-in Function: VAL = debug_jit ()
 -- Built-in Function: OLD_VAL = debug_jit (NEW_VAL)
 -- Built-in Function: debug_jit (NEW_VAL, "local")
     Query or set the internal variable that determines whether
     debugging/tracing is enabled for Octave's JIT compiler.

     When called from inside a function with the "local" option, the
     variable is changed locally for the function and any subroutines it
     calls.  The original variable value is restored when exiting the
     function.

     See also: *note jit_enable: XREFjit_enable, *note jit_startcnt:
     XREFjit_startcnt.


File: octave.info,  Node: Miscellaneous Techniques,  Next: Examples,  Prev: JIT Compiler,  Up: Vectorization and Faster Code Execution

19.6 Miscellaneous Techniques
=============================

Here are some other ways of improving the execution speed of Octave
programs.

   * Avoid computing costly intermediate results multiple times.  Octave
     currently does not eliminate common subexpressions.  Also, certain
     internal computation results are cached for variables.  For
     instance, if a matrix variable is used multiple times as an index,
     checking the indices (and internal conversion to integers) is only
     done once.

   * Be aware of lazy copies (copy-on-write).  When a copy of an object
     is created, the data is not immediately copied, but rather shared.
     The actual copying is postponed until the copied data needs to be
     modified.  For example:

          a = zeros (1000); # create a 1000x1000 matrix
          b = a; # no copying done here
          b(1) = 1; # copying done here

     Lazy copying applies to whole Octave objects such as matrices,
     cells, struct, and also individual cell or struct elements (not
     array elements).

     Additionally, index expressions also use lazy copying when Octave
     can determine that the indexed portion is contiguous in memory.
     For example:

          a = zeros (1000); # create a 1000x1000 matrix
          b = a(:,10:100);  # no copying done here
          b = a(10:100,:);  # copying done here

     This applies to arrays (matrices), cell arrays, and structs indexed
     using '()'.  Index expressions generating comma-separated lists can
     also benefit from shallow copying in some cases.  In particular,
     when A is a struct array, expressions like '{a.x}, {a(:,2).x}' will
     use lazy copying, so that data can be shared between a struct array
     and a cell array.

     Most indexing expressions do not live longer than their parent
     objects.  In rare cases, however, a lazily copied slice outlasts
     its parent, in which case it becomes orphaned, still occupying
     unnecessarily more memory than needed.  To provide a remedy working
     in most real cases, Octave checks for orphaned lazy slices at
     certain situations, when a value is stored into a "permanent"
     location, such as a named variable or cell or struct element, and
     possibly economizes them.  For example:

          a = zeros (1000); # create a 1000x1000 matrix
          b = a(:,10:100);  # lazy slice
          a = []; # the original "a" array is still allocated
          c{1} = b; # b is reallocated at this point

   * Avoid deep recursion.  Function calls to m-file functions carry a
     relatively significant overhead, so rewriting a recursion as a loop
     often helps.  Also, note that the maximum level of recursion is
     limited.

   * Avoid resizing matrices unnecessarily.  When building a single
     result matrix from a series of calculations, set the size of the
     result matrix first, then insert values into it.  Write

          result = zeros (big_n, big_m)
          for i = over:and_over
            ridx = ...
            cidx = ...
            result(ridx, cidx) = new_value ();
          endfor

     instead of

          result = [];
          for i = ever:and_ever
            result = [ result, new_value() ];
          endfor

     Sometimes the number of items can not be computed in advance, and
     stack-like operations are needed.  When elements are being
     repeatedly inserted or removed from the end of an array, Octave
     detects it as stack usage and attempts to use a smarter memory
     management strategy by pre-allocating the array in bigger chunks.
     This strategy is also applied to cell and struct arrays.

          a = [];
          while (condition)
            ...
            a(end+1) = value; # "push" operation
            ...
            a(end) = []; # "pop" operation
            ...
          endwhile

   * Avoid calling 'eval' or 'feval' excessively.  Parsing input or
     looking up the name of a function in the symbol table are
     relatively expensive operations.

     If you are using 'eval' merely as an exception handling mechanism,
     and not because you need to execute some arbitrary text, use the
     'try' statement instead.  *Note The try Statement::.

   * Use 'ignore_function_time_stamp' when appropriate.  If you are
     calling lots of functions, and none of them will need to change
     during your run, set the variable 'ignore_function_time_stamp' to
     "all".  This will stop Octave from checking the time stamp of a
     function file to see if it has been updated while the program is
     being run.


File: octave.info,  Node: Examples,  Prev: Miscellaneous Techniques,  Up: Vectorization and Faster Code Execution

19.7 Examples
=============

The following are examples of vectorization questions asked by actual
users of Octave and their solutions.

   * For a vector 'A', the following loop

          n = length (A);
          B = zeros (n, 2);
          for i = 1:length (A)
            ## this will be two columns, the first is the difference and
            ## the second the mean of the two elements used for the diff.
            B(i,:) = [A(i+1)-A(i), (A(i+1) + A(i))/2];
          endfor

     can be turned into the following one-liner:

          B = [diff(A)(:), 0.5*(A(1:end-1)+A(2:end))(:)]

     Note the usage of colon indexing to flatten an intermediate result
     into a column vector.  This is a common vectorization trick.


File: octave.info,  Node: Nonlinear Equations,  Next: Diagonal and Permutation Matrices,  Prev: Vectorization and Faster Code Execution,  Up: Top

20 Nonlinear Equations
**********************

* Menu:

* Solvers::
* Minimizers::


File: octave.info,  Node: Solvers,  Next: Minimizers,  Up: Nonlinear Equations

20.1 Solvers
============

Octave can solve sets of nonlinear equations of the form

     F (x) = 0

using the function 'fsolve', which is based on the MINPACK subroutine
'hybrd'.  This is an iterative technique so a starting point must be
provided.  This also has the consequence that convergence is not
guaranteed even if a solution exists.

 -- Function File: fsolve (FCN, X0, OPTIONS)
 -- Function File: [X, FVEC, INFO, OUTPUT, FJAC] = fsolve (FCN, ...)
     Solve a system of nonlinear equations defined by the function FCN.
     FCN should accept a vector (array) defining the unknown variables,
     and return a vector of left-hand sides of the equations.
     Right-hand sides are defined to be zeros.  In other words, this
     function attempts to determine a vector X such that 'FCN (X)' gives
     (approximately) all zeros.  X0 determines a starting guess.  The
     shape of X0 is preserved in all calls to FCN, but otherwise it is
     treated as a column vector.  OPTIONS is a structure specifying
     additional options.  Currently, 'fsolve' recognizes these options:
     "FunValCheck", "OutputFcn", "TolX", "TolFun", "MaxIter",
     "MaxFunEvals", "Jacobian", "Updating", "ComplexEqn" "TypicalX",
     "AutoScaling" and "FinDiffType".

     If "Jacobian" is "on", it specifies that FCN, called with 2 output
     arguments, also returns the Jacobian matrix of right-hand sides at
     the requested point.  "TolX" specifies the termination tolerance in
     the unknown variables, while "TolFun" is a tolerance for equations.
     Default is '1e-7' for both "TolX" and "TolFun".

     If "AutoScaling" is on, the variables will be automatically scaled
     according to the column norms of the (estimated) Jacobian.  As a
     result, TolF becomes scaling-independent.  By default, this option
     is off, because it may sometimes deliver unexpected (though
     mathematically correct) results.

     If "Updating" is "on", the function will attempt to use Broyden
     updates to update the Jacobian, in order to reduce the amount of
     Jacobian calculations.  If your user function always calculates the
     Jacobian (regardless of number of output arguments), this option
     provides no advantage and should be set to false.

     "ComplexEqn" is "on", 'fsolve' will attempt to solve complex
     equations in complex variables, assuming that the equations possess
     a complex derivative (i.e., are holomorphic).  If this is not what
     you want, should unpack the real and imaginary parts of the system
     to get a real system.

     For description of the other options, see 'optimset'.

     On return, FVAL contains the value of the function FCN evaluated at
     X, and INFO may be one of the following values:

     1
          Converged to a solution point.  Relative residual error is
          less than specified by TolFun.

     2
          Last relative step size was less that TolX.

     3
          Last relative decrease in residual was less than TolF.

     0
          Iteration limit exceeded.

     -3
          The trust region radius became excessively small.

     Note: If you only have a single nonlinear equation of one variable,
     using 'fzero' is usually a much better idea.

     Note about user-supplied Jacobians: As an inherent property of the
     algorithm, Jacobian is always requested for a solution vector whose
     residual vector is already known, and it is the last accepted
     successful step.  Often this will be one of the last two calls, but
     not always.  If the savings by reusing intermediate results from
     residual calculation in Jacobian calculation are significant, the
     best strategy is to employ OutputFcn: After a vector is evaluated
     for residuals, if OutputFcn is called with that vector, then the
     intermediate results should be saved for future Jacobian
     evaluation, and should be kept until a Jacobian evaluation is
     requested or until OutputFcn is called with a different vector, in
     which case they should be dropped in favor of this most recent
     vector.  A short example how this can be achieved follows:

          function [fvec, fjac] = user_func (x, optimvalues, state)
          persistent sav = [], sav0 = [];
          if (nargin == 1)
            ## evaluation call
            if (nargout == 1)
              sav0.x = x; # mark saved vector
              ## calculate fvec, save results to sav0.
            elseif (nargout == 2)
              ## calculate fjac using sav.
            endif
          else
            ## outputfcn call.
            if (all (x == sav0.x))
              sav = sav0;
            endif
            ## maybe output iteration status, etc.
          endif
          endfunction

          ## ...

          fsolve (@user_func, x0, optimset ("OutputFcn", @user_func, ...))

     See also: *note fzero: XREFfzero, *note optimset: XREFoptimset.

   The following is a complete example.  To solve the set of equations

     -2x^2 + 3xy   + 4 sin(y) = 6
      3x^2 - 2xy^2 + 3 cos(x) = -4

you first need to write a function to compute the value of the given
function.  For example:

     function y = f (x)
       y = zeros (2, 1);
       y(1) = -2*x(1)^2 + 3*x(1)*x(2)   + 4*sin(x(2)) - 6;
       y(2) =  3*x(1)^2 - 2*x(1)*x(2)^2 + 3*cos(x(1)) + 4;
     endfunction

   Then, call 'fsolve' with a specified initial condition to find the
roots of the system of equations.  For example, given the function 'f'
defined above,

     [x, fval, info] = fsolve (@f, [1; 2])

results in the solution

     x =

       0.57983
       2.54621

     fval =

       -5.7184e-10
        5.5460e-10

     info = 1

A value of 'info = 1' indicates that the solution has converged.

   When no Jacobian is supplied (as in the example above) it is
approximated numerically.  This requires more function evaluations, and
hence is less efficient.  In the example above we could compute the
Jacobian analytically as

     function [y, jac] = f (x)
       y = zeros (2, 1);
       y(1) = -2*x(1)^2 + 3*x(1)*x(2)   + 4*sin(x(2)) - 6;
       y(2) =  3*x(1)^2 - 2*x(1)*x(2)^2 + 3*cos(x(1)) + 4;
       if (nargout == 2)
         jac = zeros (2, 2);
         jac(1,1) =  3*x(2) - 4*x(1);
         jac(1,2) =  4*cos(x(2)) + 3*x(1);
         jac(2,1) = -2*x(2)^2 - 3*sin(x(1)) + 6*x(1);
         jac(2,2) = -4*x(1)*x(2);
       endif
     endfunction

The Jacobian can then be used with the following call to 'fsolve':

     [x, fval, info] = fsolve (@f, [1; 2], optimset ("jacobian", "on"));

which gives the same solution as before.

 -- Function File: fzero (FUN, X0)
 -- Function File: fzero (FUN, X0, OPTIONS)
 -- Function File: [X, FVAL, INFO, OUTPUT] = fzero (...)
     Find a zero of a univariate function.

     FUN is a function handle, inline function, or string containing the
     name of the function to evaluate.  X0 should be a two-element
     vector specifying two points which bracket a zero.  In other words,
     there must be a change in sign of the function between X0(1) and
     X0(2).  More mathematically, the following must hold

          sign (FUN(X0(1))) * sign (FUN(X0(2))) <= 0

     If X0 is a single scalar then several nearby and distant values are
     probed in an attempt to obtain a valid bracketing.  If this is not
     successful, the function fails.  OPTIONS is a structure specifying
     additional options.  Currently, 'fzero' recognizes these options:
     "FunValCheck", "OutputFcn", "TolX", "MaxIter", "MaxFunEvals".  For
     a description of these options, see *note optimset: XREFoptimset.

     On exit, the function returns X, the approximate zero point and
     FVAL, the function value thereof.  INFO is an exit flag that can
     have these values:

        * 1 The algorithm converged to a solution.

        * 0 Maximum number of iterations or function evaluations has
          been reached.

        * -1 The algorithm has been terminated from user output
          function.

        * -5 The algorithm may have converged to a singular point.

     OUTPUT is a structure containing runtime information about the
     'fzero' algorithm.  Fields in the structure are:

        * iterations Number of iterations through loop.

        * nfev Number of function evaluations.

        * bracketx A two-element vector with the final bracketing of the
          zero along the x-axis.

        * brackety A two-element vector with the final bracketing of the
          zero along the y-axis.

     See also: *note optimset: XREFoptimset, *note fsolve: XREFfsolve.


File: octave.info,  Node: Minimizers,  Prev: Solvers,  Up: Nonlinear Equations

20.2 Minimizers
===============

Often it is useful to find the minimum value of a function rather than
just the zeroes where it crosses the x-axis.  'fminbnd' is designed for
the simpler, but very common, case of a univariate function where the
interval to search is bounded.  For unbounded minimization of a function
with potentially many variables use 'fminunc' or 'fminsearch'.  The two
functions use different internal algorithms and some knowledge of the
objective function is required.  For functions which can be
differentiated, 'fminunc' is appropriate.  For functions with
discontinuities, or for which a gradient search would fail, use
'fminsearch'.  *Note Optimization::, for minimization with the presence
of constraint functions.  Note that searches can be made for maxima by
simply inverting the objective function ('Fto_max = -Fto_min').

 -- Function File: [X, FVAL, INFO, OUTPUT] = fminbnd (FUN, A, B,
          OPTIONS)
     Find a minimum point of a univariate function.

     FUN should be a function handle or name.  A, B specify a starting
     interval.  OPTIONS is a structure specifying additional options.
     Currently, 'fminbnd' recognizes these options: "FunValCheck",
     "OutputFcn", "TolX", "MaxIter", "MaxFunEvals".  For a description
     of these options, see *note optimset: XREFoptimset.

     On exit, the function returns X, the approximate minimum point and
     FVAL, the function value thereof.  INFO is an exit flag that can
     have these values:

        * 1 The algorithm converged to a solution.

        * 0 Maximum number of iterations or function evaluations has
          been exhausted.

        * -1 The algorithm has been terminated from user output
          function.

     Notes: The search for a minimum is restricted to be in the interval
     bound by A and B.  If you only have an initial point to begin
     searching from you will need to use an unconstrained minimization
     algorithm such as 'fminunc' or 'fminsearch'.  'fminbnd' internally
     uses a Golden Section search strategy.

     See also: *note fzero: XREFfzero, *note fminunc: XREFfminunc, *note
     fminsearch: XREFfminsearch, *note optimset: XREFoptimset.

 -- Function File: fminunc (FCN, X0)
 -- Function File: fminunc (FCN, X0, OPTIONS)
 -- Function File: [X, FVAL, INFO, OUTPUT, GRAD, HESS] = fminunc (FCN,
          ...)
     Solve an unconstrained optimization problem defined by the function
     FCN.

     FCN should accept a vector (array) defining the unknown variables,
     and return the objective function value, optionally with gradient.
     'fminunc' attempts to determine a vector X such that 'FCN (X)' is a
     local minimum.  X0 determines a starting guess.  The shape of X0 is
     preserved in all calls to FCN, but otherwise is treated as a column
     vector.  OPTIONS is a structure specifying additional options.
     Currently, 'fminunc' recognizes these options: "FunValCheck",
     "OutputFcn", "TolX", "TolFun", "MaxIter", "MaxFunEvals", "GradObj",
     "FinDiffType", "TypicalX", "AutoScaling".

     If "GradObj" is "on", it specifies that FCN, when called with 2
     output arguments, also returns the Jacobian matrix of partial first
     derivatives at the requested point.  'TolX' specifies the
     termination tolerance for the unknown variables X, while 'TolFun'
     is a tolerance for the objective function value FVAL.  The default
     is '1e-7' for both options.

     For a description of the other options, see 'optimset'.

     On return, X is the location of the minimum and FVAL contains the
     value of the objective function at X.  INFO may be one of the
     following values:

     1
          Converged to a solution point.  Relative gradient error is
          less than specified by 'TolFun'.

     2
          Last relative step size was less than 'TolX'.

     3
          Last relative change in function value was less than 'TolFun'.

     0
          Iteration limit exceeded--either maximum number of algorithm
          iterations 'MaxIter' or maximum number of function evaluations
          'MaxFunEvals'.

     -1
          Algorithm terminated by 'OutputFcn'.

     -3
          The trust region radius became excessively small.

     Optionally, 'fminunc' can return a structure with convergence
     statistics (OUTPUT), the output gradient (GRAD) at the solution X,
     and approximate Hessian (HESS) at the solution X.

     Notes: If have only a single nonlinear equation of one variable
     then using 'fminbnd' is usually a much better idea.  The algorithm
     used is a gradient search which depends on the objective function
     being differentiable.  If the function has discontinuities it may
     be better to use a derivative-free algorithm such as 'fminsearch'.

     See also: *note fminbnd: XREFfminbnd, *note fminsearch:
     XREFfminsearch, *note optimset: XREFoptimset.

 -- Function File: X = fminsearch (FUN, X0)
 -- Function File: X = fminsearch (FUN, X0, OPTIONS)
 -- Function File: [X, FVAL] = fminsearch (...)

     Find a value of X which minimizes the function FUN.  The search
     begins at the point X0 and iterates using the Nelder & Mead Simplex
     algorithm (a derivative-free method).  This algorithm is
     better-suited to functions which have discontinuities or for which
     a gradient-based search such as 'fminunc' fails.

     Options for the search are provided in the parameter OPTIONS using
     the function 'optimset'.  Currently, 'fminsearch' accepts the
     options: "TolX", "MaxFunEvals", "MaxIter", "Display".  For a
     description of these options, see 'optimset'.

     On exit, the function returns X, the minimum point, and FVAL, the
     function value thereof.

     Example usages:

          fminsearch (@(x) (x(1)-5).^2+(x(2)-8).^4, [0;0])

          fminsearch (inline ("(x(1)-5).^2+(x(2)-8).^4", "x"), [0;0])

     See also: *note fminbnd: XREFfminbnd, *note fminunc: XREFfminunc,
     *note optimset: XREFoptimset.


File: octave.info,  Node: Diagonal and Permutation Matrices,  Next: Sparse Matrices,  Prev: Nonlinear Equations,  Up: Top

21 Diagonal and Permutation Matrices
************************************

* Menu:

* Basic Usage::          Creation and Manipulation of Diagonal/Permutation Matrices
* Matrix Algebra::       Linear Algebra with Diagonal/Permutation Matrices
* Function Support::     Functions That Are Aware of These Matrices
* Example Code::         Examples of Usage
* Zeros Treatment::      Differences in Treatment of Zero Elements


File: octave.info,  Node: Basic Usage,  Next: Matrix Algebra,  Up: Diagonal and Permutation Matrices

21.1 Creating and Manipulating Diagonal/Permutation Matrices
============================================================

A diagonal matrix is defined as a matrix that has zero entries outside
the main diagonal; that is, 'D(i,j) == 0' if 'i != j'.  Most often,
square diagonal matrices are considered; however, the definition can
equally be applied to non-square matrices, in which case we usually
speak of a rectangular diagonal matrix.

   A permutation matrix is defined as a square matrix that has a single
element equal to unity in each row and each column; all other elements
are zero.  That is, there exists a permutation (vector) 'p' such that
'P(i,j) == 1' if 'j == p(i)' and 'P(i,j) == 0' otherwise.

   Octave provides special treatment of real and complex rectangular
diagonal matrices, as well as permutation matrices.  They are stored as
special objects, using efficient storage and algorithms, facilitating
writing both readable and efficient matrix algebra expressions in the
Octave language.  The special treatment may be disabled by using the
functions "disable_diagonal_matrix" and "disable_permutation_matrix".

 -- Built-in Function: VAL = disable_diagonal_matrix ()
 -- Built-in Function: OLD_VAL = disable_diagonal_matrix (NEW_VAL)
 -- Built-in Function: disable_diagonal_matrix (NEW_VAL, "local")
     Query or set the internal variable that controls whether diagonal
     matrices are stored in a special space-efficient format.  The
     default value is true.  If this option is disabled Octave will
     store diagonal matrices as full matrices.

     When called from inside a function with the "local" option, the
     variable is changed locally for the function and any subroutines it
     calls.  The original variable value is restored when exiting the
     function.

     See also: *note disable_range: XREFdisable_range, *note
     disable_permutation_matrix: XREFdisable_permutation_matrix.

 -- Built-in Function: VAL = disable_permutation_matrix ()
 -- Built-in Function: OLD_VAL = disable_permutation_matrix (NEW_VAL)
 -- Built-in Function: disable_permutation_matrix (NEW_VAL, "local")
     Query or set the internal variable that controls whether
     permutation matrices are stored in a special space-efficient
     format.  The default value is true.  If this option is disabled
     Octave will store permutation matrices as full matrices.

     When called from inside a function with the "local" option, the
     variable is changed locally for the function and any subroutines it
     calls.  The original variable value is restored when exiting the
     function.

     See also: *note disable_range: XREFdisable_range, *note
     disable_diagonal_matrix: XREFdisable_diagonal_matrix.

   The space savings are significant as demonstrated by the following
code.

     x = diag (rand (10, 1));
     xf = full (x);
     sizeof (x)
     => 80
     sizeof (xf)
     => 800

* Menu:

* Creating Diagonal Matrices::
* Creating Permutation Matrices::
* Explicit and Implicit Conversions::


File: octave.info,  Node: Creating Diagonal Matrices,  Next: Creating Permutation Matrices,  Up: Basic Usage

21.1.1 Creating Diagonal Matrices
---------------------------------

The most common and easiest way to create a diagonal matrix is using the
built-in function "diag".  The expression 'diag (v)', with V a vector,
will create a square diagonal matrix with elements on the main diagonal
given by the elements of V, and size equal to the length of V.  'diag
(v, m, n)' can be used to construct a rectangular diagonal matrix.  The
result of these expressions will be a special diagonal matrix object,
rather than a general matrix object.

   Diagonal matrix with unit elements can be created using "eye".  Some
other built-in functions can also return diagonal matrices.  Examples
include "balance" or "inv".

   Example:

       diag (1:4)
     =>
     Diagonal Matrix

        1   0   0   0
        0   2   0   0
        0   0   3   0
        0   0   0   4

       diag (1:3,5,3)

     =>
     Diagonal Matrix

        1   0   0
        0   2   0
        0   0   3
        0   0   0
        0   0   0


File: octave.info,  Node: Creating Permutation Matrices,  Next: Explicit and Implicit Conversions,  Prev: Creating Diagonal Matrices,  Up: Basic Usage

21.1.2 Creating Permutation Matrices
------------------------------------

For creating permutation matrices, Octave does not introduce a new
function, but rather overrides an existing syntax: permutation matrices
can be conveniently created by indexing an identity matrix by
permutation vectors.  That is, if Q is a permutation vector of length N,
the expression

       P = eye (n) (:, q);

will create a permutation matrix - a special matrix object.

     eye (n) (q, :)

will also work (and create a row permutation matrix), as well as

     eye (n) (q1, q2).

   For example:

       eye (4) ([1,3,2,4],:)
     =>
     Permutation Matrix

        1   0   0   0
        0   0   1   0
        0   1   0   0
        0   0   0   1

       eye (4) (:,[1,3,2,4])
     =>
     Permutation Matrix

        1   0   0   0
        0   0   1   0
        0   1   0   0
        0   0   0   1

   Mathematically, an identity matrix is both diagonal and permutation
matrix.  In Octave, 'eye (n)' returns a diagonal matrix, because a
matrix can only have one class.  You can convert this diagonal matrix to
a permutation matrix by indexing it by an identity permutation, as shown
below.  This is a special property of the identity matrix; indexing
other diagonal matrices generally produces a full matrix.

       eye (3)
     =>
     Diagonal Matrix

        1   0   0
        0   1   0
        0   0   1

       eye(3)(1:3,:)
     =>
     Permutation Matrix

        1   0   0
        0   1   0
        0   0   1

   Some other built-in functions can also return permutation matrices.
Examples include "inv" or "lu".


File: octave.info,  Node: Explicit and Implicit Conversions,  Prev: Creating Permutation Matrices,  Up: Basic Usage

21.1.3 Explicit and Implicit Conversions
----------------------------------------

The diagonal and permutation matrices are special objects in their own
right.  A number of operations and built-in functions are defined for
these matrices to use special, more efficient code than would be used
for a full matrix in the same place.  Examples are given in further
sections.

   To facilitate smooth mixing with full matrices, backward
compatibility, and compatibility with MATLAB, the diagonal and
permutation matrices should allow any operation that works on full
matrices, and will either treat it specially, or implicitly convert
themselves to full matrices.

   Instances include matrix indexing, except for extracting a single
element or a leading submatrix, indexed assignment, or applying most
mapper functions, such as "exp".

   An explicit conversion to a full matrix can be requested using the
built-in function "full".  It should also be noted that the diagonal and
permutation matrix objects will cache the result of the conversion after
it is first requested (explicitly or implicitly), so that subsequent
conversions will be very cheap.


File: octave.info,  Node: Matrix Algebra,  Next: Function Support,  Prev: Basic Usage,  Up: Diagonal and Permutation Matrices

21.2 Linear Algebra with Diagonal/Permutation Matrices
======================================================

As has been already said, diagonal and permutation matrices make it
possible to use efficient algorithms while preserving natural linear
algebra syntax.  This section describes in detail the operations that
are treated specially when performed on these special matrix objects.

* Menu:

* Expressions Involving Diagonal Matrices::
* Expressions Involving Permutation Matrices::


File: octave.info,  Node: Expressions Involving Diagonal Matrices,  Next: Expressions Involving Permutation Matrices,  Up: Matrix Algebra

21.2.1 Expressions Involving Diagonal Matrices
----------------------------------------------

Assume D is a diagonal matrix.  If M is a full matrix, then 'D*M' will
scale the rows of M.  That means, if 'S = D*M', then for each pair of
indices i,j it holds

     S(i,j) = D(i,i) * M(i,j).

   Similarly, 'M*D' will do a column scaling.

   The matrix D may also be rectangular, m-by-n where 'm != n'.  If 'm <
n', then the expression 'D*M' is equivalent to

     D(:,1:m) * M(1:m,:),

i.e., trailing 'n-m' rows of M are ignored.  If 'm > n', then 'D*M' is
equivalent to

     [D(1:n,n) * M; zeros(m-n, columns (M))],

i.e., null rows are appended to the result.  The situation for
right-multiplication 'M*D' is analogous.

   The expressions 'D \ M' and 'M / D' perform inverse scaling.  They
are equivalent to solving a diagonal (or rectangular diagonal) in a
least-squares minimum-norm sense.  In exact arithmetic, this is
equivalent to multiplying by a pseudoinverse.  The pseudoinverse of a
rectangular diagonal matrix is again a rectangular diagonal matrix with
swapped dimensions, where each nonzero diagonal element is replaced by
its reciprocal.  The matrix division algorithms do, in fact, use
division rather than multiplication by reciprocals for better numerical
accuracy; otherwise, they honor the above definition.  Note that a
diagonal matrix is never truncated due to ill-conditioning; otherwise,
it would not be of much use for scaling.  This is typically consistent
with linear algebra needs.  A full matrix that only happens to be
diagonal (and is thus not a special object) is of course treated
normally.

   Multiplication and division by diagonal matrices work efficiently
also when combined with sparse matrices, i.e., 'D*S', where D is a
diagonal matrix and S is a sparse matrix scales the rows of the sparse
matrix and returns a sparse matrix.  The expressions 'S*D', 'D\S', 'S/D'
work analogically.

   If D1 and D2 are both diagonal matrices, then the expressions

     D1 + D2
     D1 - D2
     D1 * D2
     D1 / D2
     D1 \ D2

again produce diagonal matrices, provided that normal dimension matching
rules are obeyed.  The relations used are same as described above.

   Also, a diagonal matrix D can be multiplied or divided by a scalar,
or raised to a scalar power if it is square, producing diagonal matrix
result in all cases.

   A diagonal matrix can also be transposed or conjugate-transposed,
giving the expected result.  Extracting a leading submatrix of a
diagonal matrix, i.e., 'D(1:m,1:n)', will produce a diagonal matrix,
other indexing expressions will implicitly convert to full matrix.

   Adding a diagonal matrix to a full matrix only operates on the
diagonal elements.  Thus,

     A = A + eps * eye (n)

is an efficient method of augmenting the diagonal of a matrix.
Subtraction works analogically.

   When involved in expressions with other element-by-element operators,
'.*', './', '.\' or '.^', an implicit conversion to full matrix will
take place.  This is not always strictly necessary but chosen to
facilitate better consistency with MATLAB.


File: octave.info,  Node: Expressions Involving Permutation Matrices,  Prev: Expressions Involving Diagonal Matrices,  Up: Matrix Algebra

21.2.2 Expressions Involving Permutation Matrices
-------------------------------------------------

If P is a permutation matrix and M a matrix, the expression 'P*M' will
permute the rows of M.  Similarly, 'M*P' will yield a column
permutation.  Matrix division 'P\M' and 'M/P' can be used to do inverse
permutation.

   The previously described syntax for creating permutation matrices can
actually help an user to understand the connection between a permutation
matrix and a permuting vector.  Namely, the following holds, where 'I =
eye (n)' is an identity matrix:

       I(p,:) * M = (I*M) (p,:) = M(p,:)

   Similarly,

       M * I(:,p) = (M*I) (:,p) = M(:,p)

   The expressions 'I(p,:)' and 'I(:,p)' are permutation matrices.

   A permutation matrix can be transposed (or conjugate-transposed,
which is the same, because a permutation matrix is never complex),
inverting the permutation, or equivalently, turning a row-permutation
matrix into a column-permutation one.  For permutation matrices,
transpose is equivalent to inversion, thus 'P\M' is equivalent to
'P'*M'.  Transpose of a permutation matrix (or inverse) is a
constant-time operation, flipping only a flag internally, and thus the
choice between the two above equivalent expressions for inverse
permuting is completely up to the user's taste.

   Multiplication and division by permutation matrices works efficiently
also when combined with sparse matrices, i.e., 'P*S', where P is a
permutation matrix and S is a sparse matrix permutes the rows of the
sparse matrix and returns a sparse matrix.  The expressions 'S*P',
'P\S', 'S/P' work analogically.

   Two permutation matrices can be multiplied or divided (if their sizes
match), performing a composition of permutations.  Also a permutation
matrix can be indexed by a permutation vector (or two vectors), giving
again a permutation matrix.  Any other operations do not generally yield
a permutation matrix and will thus trigger the implicit conversion.

