# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see .
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
from __future__ import division
from .minimizer import Minimizer
from .line_search_strong_wolfe import LineSearchStrongWolfe
class NonlinearCG(Minimizer):
""" Nonlinear Conjugate Gradient scheme according to Polak-Ribiere.
Algorithm 5.4 from Nocedal & Wright.
Eq. (5.41a) has been replaced by eq. (5.49)
Parameters
----------
controller : IterationController
Object that decides when to terminate the minimization.
line_searcher : LineSearch, optional
The line search algorithm to be used
References
----------
Jorge Nocedal & Stephen Wright, "Numerical Optimization", Second Edition,
2006, Springer-Verlag New York
"""
def __init__(self, controller, line_searcher=LineSearchStrongWolfe()):
self._controller = controller
self._line_searcher = line_searcher
def __call__(self, energy):
controller = self._controller
status = controller.start(energy)
if status != controller.CONTINUE:
return energy, status
f_k_minus_1 = None
p = -energy.gradient
while True:
grad_old = energy.gradient
f_k = energy.value
energy = self._line_searcher.perform_line_search(energy, p,
f_k_minus_1)
f_k_minus_1 = f_k
status = self._controller.check(energy)
if status != controller.CONTINUE:
return energy, status
grad_new = energy.gradient
gnnew = energy.gradient_norm
beta = gnnew*gnnew/(grad_new-grad_old).vdot(p).real
p = beta*p - grad_new