scipy.optimize.minimize(fun, x0, args=(), method='BFGS', jac=None, tol=None, callback=None, options={'disp': False, 'gtol': 1e-05, 'eps': 1.4901161193847656e-08, 'return_all': False, 'maxiter': None, 'norm': inf})

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

See also

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


disp : bool

Set to True to print convergence messages.

maxiter : int

Maximum number of iterations to perform.

gtol : float

Gradient norm must be less than gtol before successful termination.

norm : float

Order of norm (Inf is max, -Inf is min).

eps : float or ndarray

If jac is approximated, use this value for the step size.