# 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) 2013-2017 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
from .descent_minimizer import DescentMinimizer
from .line_search_strong_wolfe import LineSearchStrongWolfe
class RelaxedNewton(DescentMinimizer):
def __init__(self, controller, line_searcher=LineSearchStrongWolfe()):
super(RelaxedNewton, self).__init__(controller=controller,
line_searcher=line_searcher)
# FIXME: this does not look idiomatic
self.line_searcher.preferred_initial_step_size = 1.
def get_descent_direction(self, energy):
""" Calculates the descent direction according to a Newton scheme.
The descent direction is determined by weighting the gradient at the
current parameter position with the inverse local curvature, provided
by the Energy object.
Parameters
----------
energy : Energy
An instance of the Energy class which shall be minized. The
position of `energy` is used as the starting point of minization.
Returns
-------
descent_direction : Field
Returns the descent direction with proposed step length. In a
quadratic potential this corresponds to the optimal step.
"""
return -energy.curvature.inverse_times(energy.gradient)