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

Minimize a function using modified Powell’s method.

func : callable f(x,*args)

Objective function to be minimized.

x0 : ndarray

Initial guess.

args : tuple

Eextra arguments passed to func.

callback : callable

An optional user-supplied function, called after each iteration. Called as callback(xk), where xk is the current parameter vector.

direc : ndarray

Initial direction set.


(xopt, {fopt, xi, direc, iter, funcalls, warnflag}, {allvecs})

xopt : ndarray

Parameter which minimizes func.

fopt : number

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

direc : ndarray

Current direction set.

iter : int

Number of iterations.

funcalls : int

Number of function calls made.

warnflag : int
Integer warning flag:

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

allvecs : list

List of solutions at each iteration.

Other Parameters:

xtol : float
Line-search error tolerance.
ftol : float
Relative error in func(xopt) acceptable for convergence.
maxiter : int
Maximum number of iterations to perform.
maxfun : int
Maximum number of function evaluations to make.
full_output : bool
If True, fopt, xi, direc, iter, funcalls, and warnflag are returned.
disp : bool
If True, print convergence messages.
retall : bool
If True, return a list of the solution at each iteration.
Notes:Uses a modification of Powell’s method to find the minimum of a function of N variables.

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