fminsearch

Syntax

x = fminsearch(@func,x0)

x = fminsearch(@func,x0,options)

[x,fval,info,output] = fminsearch(...)

Inputs

func

The function to minimize.

x0

An estimate of the location of the minimum.

options

A struct containing option settings.

See optimset for details.

Outputs

x

The location of the function minimum.

fval

The minimum of the function.

info

The convergence status flag.

info = 1

Function value converged to within tolX.

info = 0

Reached maximum number of iterations or function calls.

output

A struct containing iteration details. The members are as follows:

iterations

The number of iterations.

nfev

The number of function evaluations.

xiter

The candidate solution at each iteration.

fvaliter

The objective function value at each iteration.

Examples

function obj = Rosenbrock(x)
    obj = (1 - x(1))^2 + 100 * (x(2) - x(1)^2)^2;
end

x0 = [-1.2, 1.0];
[x,fval] = fminsearch(@Rosenbrock, x0)

x = [Matrix] 1 x 2
1.00000  1.00000
fval = 5.29978e-14

function obj = Rosenbrock2(x, offset)
    obj = (1 - x(1))^2 + 100 * (x(2) - x(1)^2)^2 + offset;
end

handle = @(x) Rosenbrock2(x, 2);
[x,fval] = fminsearch(handle, interval)

x = [Matrix] 1 x 2
1.00000  1.00000
fval = 2

Comments

fminsearch uses the Nelder-Mead simplex algorithm, which does not require the objective function to be differentiable. When the objective function is differentiable, fminunc is generally prefereble.