# Código fuente para qiskit.algorithms.optimizers.l_bfgs_b

# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2020.
#
# obtain a copy of this license in the LICENSE.txt file in the root directory
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""Limited-memory BFGS Bound optimizer."""

from __future__ import annotations
from typing import SupportsFloat

import numpy as np

from .scipy_optimizer import SciPyOptimizer

[documentos]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,
)