### 1.7 A Nelder-Mead simplex algorithm

This function is deprecated. It is available with a slightly different interface in core Octave as ‘fminsearch’.

#### Helptext:

    NMSMAX  Nelder-Mead simplex method for direct search optimization.
           [x, fmax, nf] = NMSMAX(FUN, x0, STOPIT, SAVIT) attempts to
           maximize the function FUN, using the starting vector x0.
           The Nelder-Mead direct search method is used.
           Output arguments:
                  x    = vector yielding largest function value found,
                  fmax = function value at x,
                  nf   = number of function evaluations.
           The iteration is terminated when either
                  - the relative size of the simplex is <= STOPIT(1)
                    (default 1e-3),
                  - STOPIT(2) function evaluations have been performed
                    (default inf, i.e., no limit), or
                  - a function value equals or exceeds STOPIT(3)
                    (default inf, i.e., no test on function values).
           The form of the initial simplex is determined by STOPIT(4):
              STOPIT(4) = 0: regular simplex (sides of equal length, the default)
              STOPIT(4) = 1: right-angled simplex.
           Progress of the iteration is not shown if STOPIT(5) = 0 (default 1).
              STOPIT(6) indicates the direction (ie. minimization or
                      maximization.) Default is 1, maximization.
                      set STOPIT(6)=-1 for minimization
           If a non-empty fourth parameter string SAVIT is present, then
           `SAVE SAVIT x fmax nf' is executed after each inner iteration.
           NB: x0 can be a matrix.  In the output argument, in SAVIT saves,
               and in function calls, x has the same shape as x0.
           NMSMAX(fun, x0, STOPIT, SAVIT, P1, P2,...) allows additional
           arguments to be passed to fun, via feval(fun,x,P1,P2,...).
    References:
    N. J. Higham, Optimization by direct search in matrix computations,
       SIAM J. Matrix Anal. Appl, 14(2): 317-333, 1993.
    C. T. Kelley, Iterative Methods for Optimization, Society for Industrial
       and Applied Mathematics, Philadelphia, PA, 1999.