bundles / scipy 2.0.0.dev0+git20260907.41eeb59 / scipy / optimize / _trustregion / _minimize_trust_region
function
scipy.optimize._trustregion:_minimize_trust_region
source: build-install/usr/lib/python3.14/site-packages/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
gtolbefore 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.mapfor evaluating any numerical differentiation in parallel. This evaluation is carried out asworkers(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