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:

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 `N1000`, 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.

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.