# Quellcode für qiskit.algorithms.optimizers.l_bfgs_b

```
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2020.
#
# 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 scipy import optimize as sciopt
from .optimizer import Optimizer, OptimizerSupportLevel
[Doku]class L_BFGS_B(Optimizer): # 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/generated/scipy.optimize.fmin_l_bfgs_b.html
"""
_OPTIONS = ['maxfun', 'maxiter', 'factr', 'iprint', 'epsilon']
# pylint: disable=unused-argument
[Doku] def __init__(self,
maxfun: int = 1000,
maxiter: int = 15000,
factr: float = 10,
iprint: int = -1,
epsilon: float = 1e-08) -> None:
r"""
Args:
maxfun: Maximum number of function evaluations.
maxiter: Maximum number of iterations.
factr: The iteration stops when (f\^k - f\^{k+1})/max{\|f\^k\|,
\|f\^{k+1}\|,1} <= factr * eps, where eps is the machine precision,
which is automatically generated by the code. Typical values for
factr are: 1e12 for low accuracy; 1e7 for moderate accuracy;
10.0 for extremely high accuracy. See Notes for relationship to ftol,
which is exposed (instead of factr) by the scipy.optimize.minimize
interface to L-BFGS-B.
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 f and \|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 x; iprint > 100 print details of
every iteration including x and g.
epsilon: Step size used when approx_grad is True, for numerically
calculating the gradient
"""
super().__init__()
for k, v in list(locals().items()):
if k in self._OPTIONS:
self._options[k] = v
[Doku] def get_support_level(self):
""" Return support level dictionary """
return {
'gradient': OptimizerSupportLevel.supported,
'bounds': OptimizerSupportLevel.supported,
'initial_point': OptimizerSupportLevel.required
}
[Doku] def optimize(self, num_vars, objective_function, gradient_function=None,
variable_bounds=None, initial_point=None):
super().optimize(num_vars, objective_function, gradient_function,
variable_bounds, initial_point)
if gradient_function is None and self._max_evals_grouped > 1:
epsilon = self._options['epsilon']
gradient_function = Optimizer.wrap_function(Optimizer.gradient_num_diff,
(objective_function,
epsilon, self._max_evals_grouped))
approx_grad = bool(gradient_function is None)
sol, opt, info = sciopt.fmin_l_bfgs_b(objective_function,
initial_point, bounds=variable_bounds,
fprime=gradient_function,
approx_grad=approx_grad, **self._options)
return sol, opt, info['funcalls']
```