def SchemeUniform_OptInner(u, SB, f, bc, oracle=None):
# Use the oracle, if available, to select the active superbases only
if not (oracle is None):
SB = np.take_along_axis(SB,
np.broadcast_to(
oracle,
SB.shape[:2] + (1, ) + oracle.shape),
axis=2)
d2u = bc.Diff2(u, SB)
d2u[..., bc.not_interior] = 0. # Placeholder value to silent NaN warnings
# Generate the parameters for the low dimensional optimization problem
Q = 0.5 * np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])
dim = 2
l = -d2u
m = lp.dot_VV(SB, SB)
m = bc.as_field(m)
from agd.FiniteDifferences import as_field
Q = as_field(Q, m.shape[1:])
dim = 2
alpha = dim * f**(1 / dim)
mask = (alpha == 0)
Q = Q * np.where(mask, 1., alpha**2)
# Evaluate the non-linear functional using dense-sparse composition
residue = ad.apply(ConstrainedMaximize, Q, l, m,
shape_bound=u.shape).copy()
residue[:, mask] = np.max(l / m, axis=0)[:, mask]
return ad.max_argmax(residue, axis=0)
def SchemeUniform(u, SB, f, bc):
# Compute the finite differences along the superbase directions
d2u = bc.Diff2(u, SB)
d2u[..., bc.not_interior] = 0. # Placeholder value to silent NaN warnings
# Generate the parameters for the low dimensional optimization problem
Q = 0.5 * np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])
l = -d2u
m = lp.dot_VV(SB, SB)
# Evaluate the numerical scheme
m = bc.as_field(m)
from agd.FiniteDifferences import as_field
Q = as_field(Q, m.shape[1:])
dim = 2
alpha = dim * f**(1 / dim)
mask = (alpha == 0)
Q = Q * np.where(mask, 1., alpha**2)
residue = ConstrainedMaximize(Q, l, m).max(axis=0)
residue[mask] = np.max(l / m, axis=0).max(axis=0)[mask]
# Boundary conditions
return np.where(bc.interior, residue, u - bc.grid_values)
