Source code for implementations.sweeper_classes.generic_implicit

from pySDC.core.sweeper import Sweeper


[docs] class generic_implicit(Sweeper): """ Generic implicit sweeper, expecting lower triangular matrix type as input Attributes: QI: lower triangular matrix """ def __init__(self, params): """ Initialization routine for the custom sweeper Args: params: parameters for the sweeper """ if 'QI' not in params: params['QI'] = 'IE' # call parent's initialization routine super().__init__(params) # get QI matrix self.QI = self.get_Qdelta_implicit(qd_type=self.params.QI)
[docs] def integrate(self): """ Integrates the right-hand side Returns: list of dtype_u: containing the integral as values """ L = self.level P = L.prob me = [] # integrate RHS over all collocation nodes for m in range(1, self.coll.num_nodes + 1): # new instance of dtype_u, initialize values with 0 me.append(P.dtype_u(P.init, val=0.0)) for j in range(1, self.coll.num_nodes + 1): me[-1] += L.dt * self.coll.Qmat[m, j] * L.f[j] return me
[docs] def update_nodes(self): """ Update the u- and f-values at the collocation nodes -> corresponds to a single sweep over all nodes Returns: None """ L = self.level P = L.prob # only if the level has been touched before assert L.status.unlocked # get number of collocation nodes for easier access M = self.coll.num_nodes # update the MIN-SR-FLEX preconditioner self.updateVariableCoeffs(L.status.sweep) # gather all terms which are known already (e.g. from the previous iteration) # this corresponds to u0 + QF(u^k) - QdF(u^k) + tau # get QF(u^k) integral = self.integrate() for m in range(M): # get -QdF(u^k)_m for j in range(1, M + 1): integral[m] -= L.dt * self.QI[m + 1, j] * L.f[j] # add initial value integral[m] += L.u[0] # add tau if associated if L.tau[m] is not None: integral[m] += L.tau[m] # do the sweep for m in range(0, M): # build rhs, consisting of the known values from above and new values from previous nodes (at k+1) rhs = P.dtype_u(integral[m]) for j in range(1, m + 1): rhs += L.dt * self.QI[m + 1, j] * L.f[j] # implicit solve with prefactor stemming from the diagonal of Qd alpha = L.dt * self.QI[m + 1, m + 1] if alpha == 0: L.u[m + 1] = rhs else: L.u[m + 1] = P.solve_system(rhs, alpha, L.u[m + 1], L.time + L.dt * self.coll.nodes[m]) # update function values L.f[m + 1] = P.eval_f(L.u[m + 1], L.time + L.dt * self.coll.nodes[m]) # indicate presence of new values at this level L.status.updated = True return None
[docs] def compute_end_point(self): """ Compute u at the right point of the interval The value uend computed here is a full evaluation of the Picard formulation unless do_full_update==False Returns: None """ L = self.level P = L.prob # check if Mth node is equal to right point and do_coll_update is false, perform a simple copy if self.coll.right_is_node and not self.params.do_coll_update: # a copy is sufficient L.uend = P.dtype_u(L.u[-1]) else: # start with u0 and add integral over the full interval (using coll.weights) L.uend = P.dtype_u(L.u[0]) for m in range(self.coll.num_nodes): L.uend += L.dt * self.coll.weights[m] * L.f[m + 1] # add up tau correction of the full interval (last entry) if L.tau[-1] is not None: L.uend += L.tau[-1] return None