Find minimum of unconstrained multivariable function using derivative-free method
x = fminsearch(fun,x0)
x = fminsearch(fun,x0,options)
[x,fval] = fminsearch(...)
[x,fval,exitflag] = fminsearch(...)
[x,fval,exitflag,output] = fminsearch(...)
fminsearch finds the minimum of a scalar function of several variables, starting at an initial estimate. This is generally referred to as unconstrained nonlinear optimization.
x = fminsearch(fun,x0) starts at the point x0 and returns a value x that is a local minimizer of the function described in fun. x0 can be a scalar, vector, or matrix. fun is a function_handle.
x = fminsearch(fun,x0,options) minimizes with the optimization parameters specified in the structure options. You can define these parameters using the optimset function. fminsearch uses these options structure fields:
Level of display. 'off' displays no output; 'iter' displays output at each iteration; 'final' displays just the final output; 'notify' (default) displays output only if the function does not converge. See Iterative Display in MATLAB Mathematics for more information.
Check whether objective function values are valid. 'on' displays an error when the objective function returns a value that is complex, Inf or NaN. 'off' (the default) displays no error.
Maximum number of function evaluations allowed
Maximum number of iterations allowed
User-defined function that is called at each iteration. See Output Functions in MATLAB Mathematics for more information.
Plots various measures of progress while the algorithm executes, select from predefined plots or write your own. Pass a function handle or a cell array of function handles. The default is none ().
See Plot Functions in MATLAB Mathematics for more information.
Termination tolerance on the function value
Termination tolerance on x
fminsearch converged to a solution x.
Maximum number of function evaluations or iterations was reached.
Algorithm was terminated by the output function.
'Nelder-Mead simplex direct search'
Number of function evaluations
Number of iterations
fun is the function to be minimized. It accepts an input x and returns a scalar f, the objective function evaluated at x. The function fun can be specified as a function handle for a function file
x = fminsearch(@myfun, x0)
where myfun is a function file such as
function f = myfun(x) f = ... % Compute function value at x
or as a function handle for an anonymous function, such as
x = fminsearch(@(x)sin(x^2), x0);
Other arguments are described in the syntax descriptions above.
The Rosenbrock banana function is a classic test example for multidimensional minimization:
The minimum is at (1,1) and has the value 0. The traditional starting point is (-1.2,1). The anonymous function shown here defines the function and returns a function handle called banana:
banana = @(x)100*(x(2)-x(1)^2)^2+(1-x(1))^2;
Pass the function handle to fminsearch:
[x,fval] = fminsearch(banana,[-1.2, 1])
x = 1.0000 1.0000 fval = 8.1777e-010
This indicates that the minimizer was found to at least four decimal places with a value near zero.
If fun is parameterized, you can use anonymous functions to capture the problem-dependent parameters. For example, suppose you want to minimize the objective function myfun defined by the following function file:
function f = myfun(x,a) f = x(1)^2 + a*x(2)^2;
Note that myfun has an extra parameter a, so you cannot pass it directly to fminsearch. To optimize for a specific value of a, such as a = 1.5.
a = 1.5; % define parameter first
x = fminsearch(@(x) myfun(x,a),[0,1])
You can modify the first example by adding a parameter a to the second term of the banana function:
This changes the location of the minimum to the point [a,a^2]. To minimize this function for a specific value of a, for example a = sqrt(2), create a one-argument anonymous function that captures the value of a.
a = sqrt(2); banana = @(x)100*(x(2)-x(1)^2)^2+(a-x(1))^2;
Then the statement
[x,fval] = fminsearch(banana, [-1.2, 1], ... optimset('TolX',1e-8));
seeks the minimum [sqrt(2), 2] to an accuracy higher than the default on x.
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 variables, they must be split into real and imaginary parts.
fminsearch uses the simplex search method of Lagarias et al. . This is a direct search method that does not use numerical or analytic gradients.
If n is the length of x, a simplex in n-dimensional space is characterized by the n+1 distinct vectors that are its vertices. In two-space, a simplex is a triangle; in three-space, it is a pyramid. At each step of the search, a new point in or near the current simplex is generated. The function value at the new point is compared with the function's values at the vertices of the simplex and, usually, one of the vertices is replaced by the new point, giving a new simplex. This step is repeated until the diameter of the simplex is less than the specified tolerance.
For more information, see fminsearch Algorithm.
 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, pp. 112-147, 1998.