Find minimum of unconstrained multivariable function using derivative-free method

Nonlinear programming solver. Searches for the minimum of a problem specified by

$$\underset{x}{\mathrm{min}}f(x)$$

*f*(*x*) is a function that returns a scalar, and
*x* is a vector or a matrix; see Matrix Arguments.

finds
the minimum for `x`

= fminsearch(`problem`

)`problem`

, where `problem`

is
a structure. Create `problem`

by exporting a problem
from Optimization app, as described in Exporting Your Work.

`fminsearch`

only minimizes over the real numbers, that is,*x*must only consist of real numbers and*f*(*x*) must only return real numbers. When*x*has complex values, split*x*into real and imaginary parts.Use

`fminsearch`

to solve nondifferentiable problems or problems with discontinuities, particularly if no discontinuity occurs near the solution.`fminsearch`

is generally less efficient than`fminunc`

, especially for problems of dimension greater than two. However, when the problem is discontinuous,`fminsearch`

can be more robust than`fminunc`

.`fminsearch`

is not the preferred solver for problems that are sums of squares, that is, of the form$$\underset{x}{\mathrm{min}}{\Vert f(x)\Vert}_{2}^{2}=\underset{x}{\mathrm{min}}\left({f}_{1}{(x)}^{2}+{f}_{2}{(x)}^{2}+\mathrm{...}+{f}_{n}{(x)}^{2}\right)$$

Instead, use the

`lsqnonlin`

function, which has been optimized for problems of this form.

`fminsearch`

uses the simplex search method
of Lagarias et al. [1]. This is a direct search method that does not use numerical
or analytic gradients as in `fminunc`

.
The algorithm is described in detail in fminsearch Algorithm. The algorithm is not guaranteed to
converge to a local minimum.

[1] Lagarias, J. C., J. A. Reeds, M. H. Wright,
and P. E. Wright. “Convergence Properties of the Nelder-Mead
Simplex Method in Low Dimensions.” *SIAM Journal
of Optimization*. Vol. 9, Number 1, 1998, pp. 112–147.

`fminbnd`

| `fminunc`

| `optimset`

| `optimtool`

- Create Function Handle (MATLAB)
- Anonymous Functions (MATLAB)