# This code is part of a Qiskit project.
#
# (C) Copyright IBM 2018, 2023.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# 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
[docs]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,
)