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tamaas/python/tamaas/nonlinear_solvers/__init__.py
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__init__.py
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# -*- mode:python; coding: utf-8 -*-
#
# Copyright (©) 2016-2021 EPFL (École Polytechnique Fédérale de Lausanne),
# Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero 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 Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
Pulling solvers to nonlinear_solvers module
"""
from functools import wraps
from scipy.optimize import newton_krylov, root
from scipy.optimize.nonlin import NoConvergence
from .. import EPSolver, Logger, LogLevel, mpi
from .._tamaas import _tolerance_manager
from .._tamaas import _DFSANESolver as DFSANECXXSolver
__all__ = ['NLNoConvergence',
'DFSANESolver',
'DFSANECXXSolver',
'NewtonKrylovSolver',
'ToleranceManager']
class NLNoConvergence(Exception):
"""Convergence not reached exception"""
class ScipySolver(EPSolver):
"""
Base class for solvers wrapping SciPy routines
"""
def __init__(self, residual, _, callback=None):
super(ScipySolver, self).__init__(residual)
if mpi.size() > 1:
raise RuntimeError("Scipy solvers cannot be used with MPI; "
"DFSANECXXSolver can be used instead")
self.callback = callback
self._x = self.getStrainIncrement()
self._residual = self.getResidual()
self.options = {'ftol': 0, 'fatol': 1e-9}
def solve(self):
"""
Solve the nonlinear plasticity equation using the scipy_solve routine
"""
# For initial guess, compute the strain due to boundary tractions
# self._residual.computeResidual(self._x)
# self._x[...] = self._residual.getVector()
EPSolver.beforeSolve(self)
# Scipy root callback
def compute_residual(vec):
self._residual.computeResidual(vec)
return self._residual.getVector().copy()
# Solve
self._x[...] = self.scipy_solve(compute_residual)
# Computing displacements
self._residual.computeResidualDisplacement(self._x)
def reset(self):
"Set solution vector to zero"
self._x[...] = 0
class NewtonKrylovSolver(ScipySolver):
"""
Solve using a finite-difference Newton-Krylov method
"""
def __init__(self, residual, model=None, callback=None):
ScipySolver.__init__(self, residual, model,
callback=callback)
def scipy_solve(self, compute_residual):
"Solve R(delta epsilon) = 0 using a newton-krylov method"
try:
return newton_krylov(compute_residual, self._x,
f_tol=self.tolerance,
verbose=True, callback=self.callback)
except NoConvergence:
raise NLNoConvergence("Newton-Krylov did not converge")
class DFSANESolver(ScipySolver):
"""
Solve using a spectral residual jacobianless method
"""
def __init__(self, residual, model=None, callback=None):
ScipySolver.__init__(self, residual, model,
callback=callback)
def scipy_solve(self, compute_residual):
"Solve R(delta epsilon) = 0 using a df-sane method"
solution = root(compute_residual,
self._x,
method='df-sane',
options={'ftol': 0, 'fatol': self.tolerance},
callback=self.callback)
Logger().get(LogLevel.info) << \
"DF-SANE/Scipy: {} ({} iterations, {})".format(
solution.message,
solution.nit,
self.tolerance)
if not solution.success:
raise NLNoConvergence("DF-SANE/Scipy did not converge")
return solution.x.copy()
def ToleranceManager(start, end, rate):
"Decorator to manage tolerance of non-linear solver"
# start /= rate # just anticipating first multiplication
def actual_decorator(cls):
orig_init = cls.__init__
orig_solve = cls.solve
orig_update_state = cls.updateState
@wraps(cls.__init__)
def __init__(obj, *args, **kwargs):
orig_init(obj, *args, **kwargs)
obj.setToleranceManager(_tolerance_manager(start, end, rate))
@wraps(cls.solve)
def new_solve(obj, *args, **kwargs):
ftol = obj.tolerance
ftol *= rate
obj.tolerance = max(ftol, end)
return orig_solve(obj, *args, **kwargs)
@wraps(cls.updateState)
def updateState(obj, *args, **kwargs):
obj.tolerance = start
return orig_update_state(obj, *args, **kwargs)
cls.__init__ = __init__
# cls.solve = new_solve
# cls.updateState = updateState
return cls
return actual_decorator
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