This is documentation for an old release of SciPy (version 0.8.). Read this page Search for this page in the documentation of the latest stable release (version 1.15.1).

scipy.optimize.fmin

scipy.optimize.fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, full_output=0, disp=1, retall=0, callback=None)

Minimize a function using the downhill simplex algorithm.

Parameters :

func : callable func(x,*args)

The objective function to be minimized.

x0 : ndarray

Initial guess.

args : tuple

Extra arguments passed to func, i.e. f(x,*args).

callback : callable

Called after each iteration, as callback(xk), where xk is the current parameter vector.

Returns :

xopt : ndarray

Parameter that minimizes function.

fopt : float

Value of function at minimum: fopt = func(xopt).

iter : int

Number of iterations performed.

funcalls : int

Number of function calls made.

warnflag : int

1 : Maximum number of function evaluations made. 2 : Maximum number of iterations reached.

allvecs : list

Solution at each iteration.

Notes

Uses a Nelder-Mead simplex algorithm to find the minimum of a function of one or more variables.