scipy.optimize._optimize:fmin_ncg

Parameters

f : callable ``f(x, *args)``

Objective function to be minimized.

x0 : ndarray

Initial guess.

fprime : callable ``f'(x, *args)``

Gradient of f.

fhess_p : callable ``fhess_p(x, p, *args)``, optional

Function which computes the Hessian of f times an arbitrary vector, p.

fhess : callable ``fhess(x, *args)``, optional

Function to compute the Hessian matrix of f.

args : tuple, optional

Extra arguments passed to f, fprime, fhess_p, and fhess (the same set of extra arguments is supplied to all of these functions).

epsilon : float or ndarray, optional

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

callback : callable, optional

An optional user-supplied function which is called after each iteration. Called as callback(xk), where xk is the current parameter vector.

avextol : float, optional

Convergence is assumed when the average relative error in the minimizer falls below this amount.

maxiter : int, optional

Maximum number of iterations to perform.

full_output : bool, optional

If True, return the optional outputs.

disp : bool, optional

If True, print convergence message.

retall : bool, optional

If True, return a list of results at each iteration.

c1 : float, default: 1e-4

Parameter for Armijo condition rule.

c2 : float, default: 0.9

Parameter for curvature condition rule