def CG_Jacobi(self):
solution = doublevec.DoubleVec(0)
pc = preconditioner.JacobiPreconditioner()
solver = matrixmethod.ConjugateGradient(pc, self.tolerance,
self.max_iterations)
solver.solveMatrix(self.matrix, self.rhs, solution)
self.assertAlmostEqual(self.ref.norm(), solution.norm(), 9)
def resolve_asymmetric(self, subproblemcontext, existingSolver):
if (isinstance(existingSolver, StabilizedConjugateGradient)
and existingSolver.tolerance == self.tolerance
and existingSolver.max_iterations == self.max_iterations
and isinstance(existingSolver.preconditioner,
preconditioner.JacobiPreconditioner)):
return existingSolver
return StabilizedConjugateGradient(
preconditioner=preconditioner.JacobiPreconditioner(),
tolerance=self.tolerance,
max_iterations=self.max_iterations)
def resolve_symmetric(self, existingSolver):
if (isinstance(existingSolver, ConjugateGradient)
and existingSolver.tolerance == self.tolerance
and existingSolver.max_iterations == self.max_iterations
and isinstance(existingSolver.preconditioner,
preconditioner.JacobiPreconditioner)):
return existingSolver
return ConjugateGradient(
preconditioner=preconditioner.JacobiPreconditioner(),
tolerance=self.tolerance,
max_iterations=self.max_iterations)