scipy.optimize.fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1.0000000000000001e-05, epsilon=1.4901161193847656e-08, maxiter=None, full_output=0, disp=1, retall=0, callback=None)

Minimize a function using the Newton-CG method.

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)

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

fhess : callable fhess(x,*args)

Function to compute the Hessian matrix of f.

args : tuple

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

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

callback : callable

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


(xopt, {fopt, fcalls, gcalls, hcalls, warnflag},{allvecs})

xopt : ndarray

Parameters which minimizer f, i.e. f(xopt) == fopt.

fopt : float

Value of the function at xopt, i.e. fopt = f(xopt).

fcalls : int

Number of function calls made.

gcalls : int

Number of gradient calls made.

hcalls : int

Number of hessian calls made.

warnflag : int

Warnings generated by the algorithm. 1 : Maximum number of iterations exceeded.

allvecs : list

The result at each iteration, if retall is True (see below).

Other Parameters:

avextol : float
Convergence is assumed when the average relative error in the minimizer falls below this amount.
maxiter : int
Maximum number of iterations to perform.
full_output : bool
If True, return the optional outputs.
disp : bool
If True, print convergence message.
retall : bool
If True, return a list of results at each iteration.
  1. scikits.openopt offers a unified syntax to call this and other solvers.

2. Only one of fhess_p or fhess need to be given. If fhess is provided, then fhess_p will be ignored. If neither fhess nor fhess_p is provided, then the hessian product will be approximated using finite differences on fprime. fhess_p must compute the hessian times an arbitrary vector. If it is not given, finite-differences on fprime are used to compute it. See Wright, and Nocedal ‘Numerical Optimization’, 1999, pg. 140.

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