`scipy.optimize._optimize:_minimize_newtoncg`

source: /scipy/optimize/_optimize.py :2001

Signature

   def     _minimize_newtoncg (    fun    ,    x0    ,    args    =  ()   ,    jac    =  None   ,    hess    =  None   ,    hessp    =  None   ,    callback    =  None   ,    xtol    =  1e-05   ,    eps    =  1.4901161193847656e-08   ,    maxiter    =  None   ,    disp    =  False   ,    return_all    =  False   ,    c1    =  0.0001   ,    c2    =  0.9   ,    workers    =  None   ,   ** unknown_options      )

Summary

Minimization of scalar function of one or more variables using the Newton-CG algorithm.

Extended Summary

Note that the jac parameter (Jacobian) is required.

Parameters

disp : bool: Set to True to print convergence messages.
xtol : float: Average relative error in solution xopt acceptable for convergence.
maxiter : int: Maximum number of iterations to perform.
eps : float or ndarray: If hessp is approximated, use this value for the step size.
return_all : bool, optional: Set to True to return a list of the best solution at each of the iterations.
c1 : float, default: 1e-4: Parameter for Armijo condition rule.
c2 : float, default: 0.9: Parameter for curvature condition rule.
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).
versionadded 1.16.0

Notes

Parameters c1 and c2 must satisfy 0 < c1 < c2 < 1.

Aliases

scipy.optimize._minimize._minimize_newtoncg