Quellcode für qiskit.algorithms.optimizers.l_bfgs_b

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# (C) Copyright IBM 2018, 2020.
# This code is licensed under the Apache License, Version 2.0. You may
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"""Limited-memory BFGS Bound optimizer."""

from __future__ import annotations
from typing import SupportsFloat

import numpy as np

from .scipy_optimizer import SciPyOptimizer

[Doku]class L_BFGS_B(SciPyOptimizer): # pylint: disable=invalid-name """ Limited-memory BFGS Bound optimizer. The target goal of Limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B) is to minimize the value of a differentiable scalar function :math:`f`. This optimizer is a quasi-Newton method, meaning that, in contrast to Newtons's method, it does not require :math:`f`'s Hessian (the matrix of :math:`f`'s second derivatives) when attempting to compute :math:`f`'s minimum value. Like BFGS, L-BFGS is an iterative method for solving unconstrained, non-linear optimization problems, but approximates BFGS using a limited amount of computer memory. L-BFGS starts with an initial estimate of the optimal value, and proceeds iteratively to refine that estimate with a sequence of better estimates. The derivatives of :math:`f` are used to identify the direction of steepest descent, and also to form an estimate of the Hessian matrix (second derivative) of :math:`f`. L-BFGS-B extends L-BFGS to handle simple, per-variable bound constraints. Uses ``scipy.optimize.fmin_l_bfgs_b``. For further detail, please refer to https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html """ _OPTIONS = ["maxfun", "maxiter", "ftol", "iprint", "eps"] # pylint: disable=unused-argument def __init__( self, maxfun: int = 15000, maxiter: int = 15000, ftol: SupportsFloat = 10 * np.finfo(float).eps, iprint: int = -1, eps: float = 1e-08, options: dict | None = None, max_evals_grouped: int = 1, **kwargs, ): r""" Args: maxfun: Maximum number of function evaluations. maxiter: Maximum number of iterations. ftol: The iteration stops when :math:`(f^k - f^{k+1}) / \max\{|f^k|, |f^{k+1}|,1\} \leq \text{ftol}`. iprint: Controls the frequency of output. ``iprint < 0`` means no output; ``iprint = 0`` print only one line at the last iteration; ``0 < iprint < 99`` print also :math:`f` and :math:`|\text{proj} g|` every iprint iterations; ``iprint = 99`` print details of every iteration except n-vectors; ``iprint = 100`` print also the changes of active set and final :math:`x`; ``iprint > 100`` print details of every iteration including :math:`x` and :math:`g`. eps: If jac is approximated, use this value for the step size. 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__( method="L-BFGS-B", options=options, max_evals_grouped=max_evals_grouped, **kwargs, )