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Fix issue for sparse matrix conversions #538

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merged 5 commits into from
Apr 5, 2025
Merged

Fix issue for sparse matrix conversions #538

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41f2f47
sparse: fix sorting of indices when needed
jcarpent Mar 26, 2025
9545cce
test/simplicial: test SimplicialLLT::solve for sparse matrix
jcarpent Mar 26, 2025
deadcce
changelog: sync
jcarpent Mar 26, 2025
ea0b9d9
test: fix logic
jcarpent Mar 26, 2025
58cda91
test/scipy: fix random generator for old scipy versions
jcarpent Apr 4, 2025
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4 changes: 4 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -6,6 +6,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).

## [Unreleased]

### Fixed

- Fix handling of non sorted sparse matrix ([#538](https://github.com/stack-of-tasks/eigenpy/pull/538))

## [3.10.3] - 2025-02-11

### Added
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6 changes: 5 additions & 1 deletion include/eigenpy/sparse/eigen-from-python.hpp
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@@ -1,5 +1,5 @@
//
// Copyright (c) 2024 INRIA
// Copyright (c) 2024-2025 INRIA
//

#ifndef __eigenpy_sparse_eigen_from_python_hpp__
Expand Down Expand Up @@ -156,6 +156,10 @@ void eigen_sparse_matrix_from_py_construct(
}
MapMatOrRefType sparse_map(m, n, nnz, indptr.data(), indices_ptr, data_ptr);

#if EIGEN_VERSION_AT_LEAST(3, 4, 90)
sparse_map.sortInnerIndices();
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Probably at some point we would like to add a check (and throw an exception) if the indices are unsorted and the Eigen version is below 3.4.90 ?

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Yes, sure.

#endif

new (raw_ptr) MatOrRefType(sparse_map);
}

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12 changes: 12 additions & 0 deletions unittest/python/decompositions/sparse/test_SimplicialLLT.py
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@@ -1,4 +1,5 @@
import numpy as np
import scipy
from scipy.sparse import csc_matrix

import eigenpy
Expand Down Expand Up @@ -30,3 +31,14 @@
llt.analyzePattern(A)
llt.factorize(A)
permutation = llt.permutationP()

X_sparse = scipy.sparse.random(dim, 10)
B_sparse = A.dot(X_sparse)
B_sparse = B_sparse.tocsc(True)

if not B_sparse.has_sorted_indices:
B_sparse.sort_indices()

X_est = llt.solve(B_sparse)
assert eigenpy.is_approx(X_est.toarray(), X_sparse.toarray())
assert eigenpy.is_approx(A.dot(X_est.toarray()), B_sparse.toarray())