Source code for qiskit_algorithms.optimizers.tnc

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"""Truncated Newton (TNC) optimizer."""
from __future__ import annotations

from .scipy_optimizer import SciPyOptimizer

[docs]class TNC(SciPyOptimizer): """ Truncated Newton (TNC) optimizer. TNC uses a truncated Newton algorithm to minimize a function with variables subject to bounds. This algorithm uses gradient information; it is also called Newton Conjugate-Gradient. It differs from the :class:`CG` method as it wraps a C implementation and allows each variable to be given upper and lower bounds. Uses scipy.optimize.minimize TNC For further detail, please refer to See """ _OPTIONS = ["maxiter", "disp", "accuracy", "ftol", "xtol", "gtol", "eps"] # pylint: disable=unused-argument def __init__( self, maxiter: int = 100, disp: bool = False, accuracy: float = 0, ftol: float = -1, xtol: float = -1, gtol: float = -1, tol: float | None = None, eps: float = 1e-08, options: dict | None = None, max_evals_grouped: int = 1, **kwargs, ) -> None: """ Args: maxiter: Maximum number of function evaluation. disp: Set to True to print convergence messages. accuracy: Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0. ftol: Precision goal for the value of f in the stopping criterion. If ftol < 0.0, ftol is set to 0.0 defaults to -1. xtol: Precision goal for the value of x in the stopping criterion (after applying x scaling factors). If xtol < 0.0, xtol is set to sqrt(machine_precision). Defaults to -1. gtol: Precision goal for the value of the projected gradient in the stopping criterion (after applying x scaling factors). If gtol < 0.0, gtol is set to 1e-2 * sqrt(accuracy). Setting it to 0.0 is not recommended. Defaults to -1. tol: Tolerance for termination. eps: Step size used for numerical approximation of the Jacobian. options: A dictionary of solver options. max_evals_grouped: Max number of default gradient evaluations performed simultaneously. kwargs: additional kwargs for scipy.optimize.minimize. """ if options is None: options = {} for k, v in list(locals().items()): if k in self._OPTIONS: options[k] = v super().__init__( "TNC", options=options, tol=tol, max_evals_grouped=max_evals_grouped, **kwargs, )