### 25.2 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

`[x, obj, info, lambda] =` qp `(x0, H)` ¶
`[x, obj, info, lambda] =` qp `(x0, H, q)` ¶
`[x, obj, info, lambda] =` qp `(x0, H, q, A, b)` ¶
`[x, obj, info, lambda] =` qp `(x0, H, q, A, b, lb, ub)` ¶
`[x, obj, info, lambda] =` qp `(x0, H, q, A, b, lb, ub, A_lb, A_in, A_ub)` ¶
`[x, obj, info, lambda] =` qp `(…, options)` ¶

  Solve a quadratic program (QP).

  Solve the quadratic program defined by

      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_in, A_lb, A_ub) may be set to the empty matrix (`[]`) if not present. The constraints A and A_in are matrices with each row representing a single constraint. The other bounds are scalars or vectors depending on the number of constraints. The algorithm is faster if the initial guess is feasible.

  options is a structure specifying additional parameters which control the algorithm. Currently, `qp` recognizes these options: `"MaxIter"`, `"TolX"`.

  `"MaxIter"` proscribes the maximum number of algorithm iterations before optimization is halted. The default value is 200. The value must be a positive integer.

  `"TolX"` specifies the termination tolerance for the unknown variables x. The default is `sqrt (eps)` or approximately 1e-8.

  On return, x is the location of the minimum and fval contains the value of the objective function at x.

  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.

  See also: sqp.

`x =` pqpnonneg `(c, d)` ¶
`x =` pqpnonneg `(c, d, x0)` ¶
`x =` pqpnonneg `(c, d, x0, options)` ¶
`[x, minval] =` pqpnonneg `(…)` ¶
`[x, minval, exitflag] =` pqpnonneg `(…)` ¶
`[x, minval, exitflag, output] =` pqpnonneg `(…)` ¶
`[x, minval, exitflag, output, lambda] =` pqpnonneg `(…)` ¶

  Minimize `(1/2 * x' * c * x + d' * x)` subject to `x >= 0`.

  c and d must be real matrices, and c must be symmetric and positive definite.

  x0 is an optional initial guess for the solution x.

  options is an options structure to change the behavior of the algorithm (see `optimset`). `pqpnonneg` recognizes one option: `"MaxIter"`.

  Outputs:

  x

    The solution matrix

  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

    Lagrange multipliers. If these are nonzero, the corresponding x values should be zero, indicating the solution is pressed up against a coordinate plane. The magnitude indicates how much the residual would improve if the `x >= 0` constraints were relaxed in that direction.

  See also: lsqnonneg, qp, optimset.