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Use upper triangle of B to enforce symmetry rather than averaging errors #42

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Merged
timothy-nunn merged 1 commit into from
Dec 4, 2025
Merged

Use upper triangle of B to enforce symmetry rather than averaging errors #42

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19 changes: 14 additions & 5 deletions src/pyvmcon/vmcon.py
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Original file line number Diff line number Diff line change
Expand Up @@ -519,14 +519,15 @@ def _revise_B(current_B: MatrixType, ksi: VectorType, gamma: VectorType) -> Matr
# Bxx^TB is mathematically guaranteed to be symmetric when
# B is a real matrix and x is a real vector.
# This is because of slight numerical rounding error.
# To ensure this error does not accumulate over time and become a problem,
# we 'symmetrise' the term by meeting in the middle of the absolute error.
term2a = (term2a + term2a.T) / 2.0
# VMCON requires B is symmetric therefore we force that to avoid potential
# errors later on (e.g. some cases where B is no longer positive semi-definite)
triangle_indices = np.tril_indices_from(term2a)
term2a[triangle_indices] = term2a.T[triangle_indices]

return (
current_B
- (term2a / (ksi.T @ current_B @ ksi).item())
+ (np.outer(gamma, gamma) / (ksi.T @ gamma))
+ (np.outer(gamma, gamma) / (ksi.T @ gamma).item())
)


Expand Down Expand Up @@ -565,7 +566,15 @@ def calculate_new_B(
logger.error("All xi (ksi) components are 0")
ksi[:] = 1e-10

return _revise_B(B, ksi, gamma)
new_B = _revise_B(B, ksi, gamma)

if (np.linalg.eigvals(new_B) < 0.0).any():
logger.error(
"The B matrix has at least one negative eigenvalue meaning it is no"
" longer positive semi-definite. This will cause a crash next iteration!"
)

return new_B


def _find_out_of_bounds_vars(higher: VectorType, lower: VectorType) -> list[str]:
Expand Down