fminunc

Syntax

x = fminunc(@func,x0)

x = fminunc(@func,x0,options)

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

Inputs

func

The function to minimize. See the optimset option GradObj for details.

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 = 4

Relative step size converged to within tolX.

info = 3

Relative function value converged to within tolFun.

info = 2

Step size converged to within tolX.

info = 1

Function value converged to within tolFun.

info = 0

Reached maximum number of iterations or function calls, or the algorithm aborted because it was not converging.

info = -3

Trust region became too small to continue.

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] = fminunc(@Rosenbrock, x0)

x = [Matrix] 1 x 2
1.00000  1.00000
fval = 1.29122e-15

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] = fminunc(handle, x0)

x = [Matrix] 1 x 2
0.99999  0.99999
fval = 2

Comments

fminunc uses a quasi-Netwon algorithm with damped BFGS updates and a trust region method.

Options for convergence tolerance controls and analytical derivatives are specified with optimset.

To pass additional parameters to a function argument, use an anonymous function.