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# minimize(method=’Powell’)

`scipy.optimize.``minimize`(fun, x0, args=(), method='Powell', tol=None, callback=None, options={'disp': False, 'return_all': False, 'maxiter': None, 'direc': None, 'func': None, 'maxfev': None, 'xtol': 0.0001, 'ftol': 0.0001})

  Minimization of scalar function of one or more variables using the modified Powell algorithm.

  See also

  For documentation for the rest of the parameters, see `scipy.optimize.minimize`

  Options:

  disp : bool

  > Set to True to print convergence messages.

  xtol : float

  > Relative error in solution xopt acceptable for convergence.

  ftol : float

  > Relative error in `fun(xopt)` acceptable for convergence.

  maxiter, maxfev : int

  > Maximum allowed number of iterations and function evaluations. Will default to `N*1000`, where `N` is the number of variables, if neither maxiter or maxfev is set. If both maxiter and maxfev are set, minimization will stop at the first reached.

  direc : ndarray

  > Initial set of direction vectors for the Powell method.