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Merged
ralberd merged 3 commits into from
Sep 21, 2026
Merged

Update numpy and sksparse changes #124

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7f3739e
Update uses of sksparse package to conform to its changed API in v0.5
btalamini Sep 18, 2026
85f9423
Fix complex number-valued quadrature point locations
btalamini Sep 19, 2026
e5e6677
Adjust test tolerance that was unrealistically tight
btalamini Sep 19, 2026
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2 changes: 1 addition & 1 deletion optimism/Interpolants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,7 @@ def make_parent_element_1d(degree):
def get_lobatto_nodes_1d(degree):
p = onp.polynomial.Legendre.basis(degree, domain=[0.0, 1.0])
dp = p.deriv()
xInterior = dp.roots()
xInterior = np.real(dp.roots())
xn = np.hstack((np.array([0.0]), xInterior, np.array([1.0])))
return xn

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24 changes: 6 additions & 18 deletions optimism/SparseCholesky.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@

import numpy as onp

from sksparse.cholmod import analyze, cholesky
from sksparse import cholmod
from sksparse.cholmod import CholmodNotPositiveDefiniteError as NotPosDefError

from optimism.JaxConfig import *
Expand All @@ -21,15 +21,15 @@ def factorize(self):
# we can improve this later if we are inclined
assert isspmatrix_csc(self.A), \
"Preconditioner matrix is not in a valid sparse format"
self.Precond = analyze(self.A, mode='supernodal',
ordering_method='nesdis')
self.Precond = cholmod.CholeskyFactor(self.A, sym_kind="sym", supernodal_mode="supernodal",
order="metis")

attempt = 0
maxAttempts = 10
while attempt < maxAttempts:
try:
print('Factorizing preconditioner')
self.Precond.cholesky_inplace(self.A)
self.Precond.factorize(self.A)
except NotPosDefError:
attempt += 1
print('Cholesky failed, assembling preconditioner', attempt)
Expand All @@ -41,7 +41,7 @@ def factorize(self):
if attempt == maxAttempts:
print("Cholesky failed too many times, using identity preconditioner")
self.A = identity(self.A.shape[0], format='csc')
self.Precond.cholesky_inplace(self.A)
self.Precond.factorize(self.A)


def update(self, new_stiffness_func):
Expand All @@ -50,9 +50,7 @@ def update(self, new_stiffness_func):


def apply(self, b):
if type(b) == type(np.array([])):
b = onp.array(b, copy=False)
return self.Precond(b)
return np.asarray(self.Precond.solve(onp.array(b, copy=True)))


def apply_transpose(self, b):
Expand All @@ -67,16 +65,6 @@ def multiply_by_transpose(self, x):
return self.A.T.dot(x)


def check_stability(self, x, p):
A = self.stiffness_func(x, p)
try:
self.Precond.cholesky(A)
print("Jacobian is stable.")
except NotPosDefError as e:
print(e)
print("Jacobian is unstable.")


def get_diagonal_stiffness(self):
return self.A.diagonal()

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2 changes: 1 addition & 1 deletion optimism/test/test_TensorMath.py
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Expand Up @@ -265,7 +265,7 @@ def test_right_polar_decomp(self):
# U is symmetric
self.assertArrayNear(U, TensorMath.sym(U), 14)
# RU = F
self.assertArrayNear(R@U, F, 14)
self.assertArrayNear(R@U, F, 13)


if __name__ == '__main__':
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