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### 24.2 Quadratic Programming

Octave can also solve Quadratic Programming problems, this is

         min 0.5 x'*H*x + x'*q

subject to

              A*x = b
              lb <= x <= ub
              A_lb <= A_in*x <= A_ub

— Function File: [x, obj, info, lambda] = qp (x0, H)
— Function File: [x, obj, info, lambda] = qp (x0, H, q)
— Function File: [x, obj, info, lambda] = qp (x0, H, q, A, b)
— Function File: [x, obj, info, lambda] = qp (x0, H, q, A, b, lb, ub)
— Function File: [x, obj, info, lambda] = qp (x0, H, q, A, b, lb, ub, A_lb, A_in, A_ub)
— Function File: [x, obj, info, lambda] = qp (..., options)

> Solve the quadratic program

>                    min 0.5 x'*H*x + x'*q
>                     x

> subject to

>                    A*x = b
>                    lb <= x <= ub
>                    A_lb <= A_in*x <= A_ub

> using a null-space active-set method.

> Any bound (A, b, lb, ub, A_lb, A_ub) may be set to the empty matrix (`[]`) if not present. If the initial guess is feasible the algorithm is faster.

> options
>   An optional structure containing the following parameter(s) used to define the behavior of the solver. Missing elements in the structure take on default values, so you only need to set the elements that you wish to change from the default.
>   `MaxIter (default: 200)`
>     Maximum number of iterations.
> info
>   Structure containing run-time information about the algorithm. The following fields are defined:
>   `solveiter`
>     The number of iterations required to find the solution.
>   `info`
>     An integer indicating the status of the solution.
>     0
>       The problem is feasible and convex. Global solution found.
>     1
>       The problem is not convex. Local solution found.
>     2
>       The problem is not convex and unbounded.
>     3
>       Maximum number of iterations reached.
>     6
>       The problem is infeasible.

— Function File: x = pqpnonneg (c, d)
— Function File: x = pqpnonneg (c, d, x0)
— Function File: [x, minval] = pqpnonneg (...)
— Function File: [x, minval, exitflag] = pqpnonneg (...)
— Function File: [x, minval, exitflag, output] = pqpnonneg (...)
— Function File: [x, minval, exitflag, output, lambda] = pqpnonneg (...)

> Minimize `1/2*x'*c*x + d'*x` subject to x`>= 0`. c and d must be real, and c must be symmetric and positive definite. x0 is an optional initial guess for x.

> Outputs:

> - minval

>   The minimum attained model value, 1/2*xmin'*c*xmin + d'*xmin

> - exitflag

>   An indicator of convergence. 0 indicates that the iteration count was exceeded, and therefore convergence was not reached; >0 indicates that the algorithm converged. (The algorithm is stable and will converge given enough iterations.)

> - output

>   A structure with two fields:

>   - "algorithm": The algorithm used ("nnls")
>   - "iterations": The number of iterations taken.
> - lambda

>   Not implemented.

> See also: optimset, lsqnonneg, qp.