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# scipy.optimize.fmin_powell

`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.

  Parameters: | 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.
  Returns: | (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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