def UpdateSolution(self, r, x):
self.V.appendleft(deepcopy(r))
self.W.appendleft(deepcopy(x))
row = len(r)
col = len(self.V) - 1
k = col
if k == 0:
## For the first iteration, do relaxation only
if self.echo_level > 3:
classprint(
self._ClassName(),
"Doing relaxation in the first iteration with factor = ",
"{0:.1g}".format(self.alpha))
return self.alpha * r
else:
self.F = np.empty(shape=(col, row)) # will be transposed later
self.X = np.empty(shape=(col, row)) # will be transposed later
for i in range(0, col):
self.F[i] = self.V[i] - self.V[i + 1]
self.X[i] = self.W[i] - self.W[i + 1]
self.F = self.F.T
self.X = self.X.T
#compute Moore-Penrose inverse of F^T F
A = np.linalg.pinv(self.F.T @ self.F)
switch = (self.iteration_counter + 1) / self.p
classprint(magenta(self.iteration_counter))
if switch.is_integer() == True:
B = self.beta * np.identity(row) - (
self.X + self.beta * self.F) @ A @ self.F.T
if self.echo_level > 3:
classprint(self._ClassName(),
blue("Compute B with Anderson"))
else:
B = self.alpha * np.identity(row)
if self.echo_level > 3:
classprint(self._ClassName(),
red("Constant underrelaxtion"))
delta_x = B @ r
self.iteration_counter += 1
return delta_x
def UpdateSolution(self, r, x):
if self.echo_level > 3:
classprint(self._ClassName(), "Doing relaxation with factor = ",
"{0:.1g}".format(self.alpha))
delta_x = self.alpha * r
return delta_x