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Add test coverage for matrix lpNorm, RowMajor partial reductions, selfadjoint boundaries

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libeigen/eigen!2289

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
This commit is contained in:
Rasmus Munk Larsen
2026-03-12 14:45:51 -07:00
parent 15cae83485
commit 356a9ba1da
3 changed files with 177 additions and 0 deletions
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57
test/vectorwiseop.cpp
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@@ -321,6 +321,58 @@ void vectorwiseop_mixedscalar() {
VERIFY_IS_CWISE_EQUAL(c, d);
}
// Test partial reductions on RowMajor matrices.
// The existing tests only use ColMajor matrices.
template <typename Scalar>
void vectorwiseop_rowmajor() {
typedef Matrix<Scalar, Dynamic, Dynamic, RowMajor> RowMajorMatrix;
typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> ColMajorMatrix;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<RealScalar, 1, Dynamic> RealRowVectorType;
typedef Matrix<RealScalar, Dynamic, 1> RealColVectorType;
const Index rows = 7;
const Index cols = 11;
ColMajorMatrix mc = ColMajorMatrix::Random(rows, cols);
RowMajorMatrix mr = mc; // same data, different storage
// Partial reductions should give the same result regardless of storage order.
VERIFY_IS_APPROX(mc.colwise().sum(), mr.colwise().sum());
VERIFY_IS_APPROX(mc.rowwise().sum(), mr.rowwise().sum());
VERIFY_IS_APPROX(mc.colwise().prod(), mr.colwise().prod());
VERIFY_IS_APPROX(mc.rowwise().prod(), mr.rowwise().prod());
VERIFY_IS_APPROX(mc.colwise().squaredNorm(), mr.colwise().squaredNorm());
VERIFY_IS_APPROX(mc.rowwise().squaredNorm(), mr.rowwise().squaredNorm());
VERIFY_IS_APPROX(mc.colwise().norm(), mr.colwise().norm());
VERIFY_IS_APPROX(mc.rowwise().norm(), mr.rowwise().norm());
RealRowVectorType rr_c, rr_r;
RealColVectorType rc_c, rc_r;
rr_c = mc.real().colwise().minCoeff();
rr_r = mr.real().colwise().minCoeff();
VERIFY_IS_APPROX(rr_c, rr_r);
rr_c = mc.real().colwise().maxCoeff();
rr_r = mr.real().colwise().maxCoeff();
VERIFY_IS_APPROX(rr_c, rr_r);
rc_c = mc.real().rowwise().minCoeff();
rc_r = mr.real().rowwise().minCoeff();
VERIFY_IS_APPROX(rc_c, rc_r);
rc_c = mc.real().rowwise().maxCoeff();
rc_r = mr.real().rowwise().maxCoeff();
VERIFY_IS_APPROX(rc_c, rc_r);
// Broadcast operations
typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
ColVectorType cv = ColVectorType::Random(rows);
RowVectorType rv = RowVectorType::Random(cols);
VERIFY_IS_APPROX(ColMajorMatrix(mc.colwise() + cv), ColMajorMatrix(mr.colwise() + cv));
VERIFY_IS_APPROX(ColMajorMatrix(mc.rowwise() + rv), ColMajorMatrix(mr.rowwise() + rv));
VERIFY_IS_APPROX(ColMajorMatrix(mc.colwise() - cv), ColMajorMatrix(mr.colwise() - cv));
VERIFY_IS_APPROX(ColMajorMatrix(mc.rowwise() - rv), ColMajorMatrix(mr.rowwise() - rv));
}
EIGEN_DECLARE_TEST(vectorwiseop) {
CALL_SUBTEST_1(vectorwiseop_array(Array22cd()));
CALL_SUBTEST_2(vectorwiseop_array(Array<double, 3, 2>()));
@@ -336,4 +388,9 @@ EIGEN_DECLARE_TEST(vectorwiseop) {
CALL_SUBTEST_8(vectorwiseop_mixedscalar());
CALL_SUBTEST_9(vectorwiseop_array_integer(
ArrayXXi(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
// RowMajor partial reductions (deterministic, outside g_repeat).
CALL_SUBTEST_10(vectorwiseop_rowmajor<float>());
CALL_SUBTEST_10(vectorwiseop_rowmajor<double>());
CALL_SUBTEST_10(vectorwiseop_rowmajor<std::complex<float>>());
}