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bundles / scipy 1.18.0 / scipy / optimize / _trustregion / _minimize_trust_region

function

scipy.optimize._trustregion:_minimize_trust_region

source: /scipy/optimize/_trustregion.py:118

Signature

def _minimize_trust_region(fun, x0, args = (), jac = None, hess = None, hessp = None, subproblem = None, initial_trust_radius = 1.0, max_trust_radius = 1000.0, eta = 0.15, gtol = 0.0001, maxiter = None, disp = False, return_all = False, callback = None, inexact = True, workers = None, subproblem_maxiter = None, **unknown_options)

Summary

Minimization of scalar function of one or more variables using a trust-region algorithm.

Extended Summary

Options for the trust-region algorithm are:

initial_trust_radius

Initial trust radius.

max_trust_radius

Never propose steps that are longer than this value.

eta

Trust region related acceptance stringency for proposed steps.

gtol

Gradient norm must be less than gtol before successful termination.

maxiter

Maximum number of iterations to perform.

disp

If True, print convergence message.

inexact

Accuracy to solve subproblems. If True requires less nonlinear iterations, but more vector products. Only effective for method trust-krylov.

workers

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). Only for 'trust-krylov', 'trust-ncg'.

subproblem_maxiter

Maximum number of iterations to perform per subproblem. Only affects trust-exact. Default is 25.

This function is called by the minimize function. It is not supposed to be called directly.

Aliases

  • scipy.optimize._trustregion._minimize_trust_region