relaxed_newton.py 2.26 KB
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# NIFTy
# Copyright (C) 2017  Theo Steininger
#
# Author: Theo Steininger
#
# 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 <http://www.gnu.org/licenses/>.
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from .quasi_newton_minimizer import QuasiNewtonMinimizer
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from .line_searching import LineSearchStrongWolfe
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class RelaxedNewton(QuasiNewtonMinimizer):
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Jakob Knollmueller committed
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    """ A implementation of the relaxed Newton minimization scheme.
    The relaxed Newton minimization exploits gradient and curvature information to
    propose a step. A linesearch optimizes along this direction.

    Parameter
    ---------
    line_searcher : LineSearch,
        An implementation of a line-search algorithm.
    callback :
    convergence_tolerance : float,
        Specifies the
    convergence_level :
    iteration_limit :

    """
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    def __init__(self, line_searcher=LineSearchStrongWolfe(), callback=None,
                 convergence_tolerance=1E-4, convergence_level=3,
                 iteration_limit=None):
        super(RelaxedNewton, self).__init__(
                                line_searcher=line_searcher,
                                callback=callback,
                                convergence_tolerance=convergence_tolerance,
                                convergence_level=convergence_level,
                                iteration_limit=iteration_limit)

        self.line_searcher.prefered_initial_step_size = 1.

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    def _get_descend_direction(self, energy):
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        gradient = energy.gradient
        curvature = energy.curvature
        descend_direction = curvature.inverse_times(gradient)
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        return descend_direction * -1
        #norm = descend_direction.norm()
#        if norm != 1:
#            return descend_direction / -norm
#        else:
#            return descend_direction * -1