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