scipy.optimize._optimize:_minimize_bfgs

Parameters

disp : bool

Set to True to print convergence messages.

maxiter : int

Maximum number of iterations to perform.

gtol : float

Terminate successfully if gradient norm is less than gtol.

norm : float

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

eps : float or ndarray

If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences.

return_all : bool, optional

Set to True to return a list of the best solution at each of the iterations.

finite_diff_rel_step : None or array_like, optional

If jac in ['2-point', '3-point', 'cs'] the relative step size to use for numerical approximation of the jacobian. The absolute step size is computed as h = rel_step * sign(x) * max(1, abs(x)), possibly adjusted to fit into the bounds. For jac='3-point' the sign of h is ignored. If None (default) then step is selected automatically.

xrtol : float, default: 0

Relative tolerance for x. Terminate successfully if step size is less than xk * xrtol where xk is the current parameter vector.

c1 : float, default: 1e-4

Parameter for Armijo condition rule.

c2 : float, default: 0.9

Parameter for curvature condition rule.

hess_inv0 : None or ndarray, optional

Initial inverse hessian estimate, shape (n, n). If None (default) then the identity matrix is used.

workers : int, map-like callable, optional

A map-like callable, such as multiprocessing.Pool.map for evaluating any numerical differentiation in parallel. This evaluation is carried out as workers(fun, iterable).