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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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52
test/array_for_matrix.cpp
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@@ -273,6 +273,45 @@ void resize(const MatrixTraits& t) {
VERIFY(a1.size() == cols);
}
// Test lpNorm for matrices (not just vectors).
// lpNorm treats the matrix as a flat coefficient vector,
// so the reference is computed element-by-element.
template <typename MatrixType>
void lpNorm_matrix(const MatrixType& m) {
using std::abs;
using std::pow;
using std::sqrt;
typedef typename MatrixType::RealScalar RealScalar;
Index rows = m.rows();
Index cols = m.cols();
MatrixType u = MatrixType::Random(rows, cols);
// L1 norm
RealScalar ref_l1(0);
for (Index j = 0; j < cols; ++j)
for (Index i = 0; i < rows; ++i) ref_l1 += abs(u(i, j));
VERIFY_IS_APPROX(u.template lpNorm<1>(), ref_l1);
// L2 norm
RealScalar ref_l2_sq(0);
for (Index j = 0; j < cols; ++j)
for (Index i = 0; i < rows; ++i) ref_l2_sq += numext::abs2(u(i, j));
VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(ref_l2_sq));
// L-Infinity norm
RealScalar ref_linf(0);
for (Index j = 0; j < cols; ++j)
for (Index i = 0; i < rows; ++i) ref_linf = (std::max)(ref_linf, abs(u(i, j)));
VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), ref_linf);
// L5 norm
RealScalar ref_l5(0);
for (Index j = 0; j < cols; ++j)
for (Index i = 0; i < rows; ++i) ref_l5 += pow(abs(u(i, j)), RealScalar(5));
VERIFY_IS_APPROX(u.template lpNorm<5>(), pow(ref_l5, RealScalar(1) / RealScalar(5)));
}
template <int>
void regression_bug_654() {
ArrayXf a = RowVectorXf(3);
@@ -346,4 +385,17 @@ EIGEN_DECLARE_TEST(array_for_matrix) {
}
CALL_SUBTEST_6(regression_bug_654<0>());
CALL_SUBTEST_6(regrrssion_bug_1410<0>());
// lpNorm for matrices (not just vectors)
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_9(lpNorm_matrix(Matrix2f()));
CALL_SUBTEST_9(lpNorm_matrix(Matrix4d()));
CALL_SUBTEST_9(lpNorm_matrix(
MatrixXf(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_9(lpNorm_matrix(MatrixXcd(internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2),
internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2))));
}
// empty matrix
CALL_SUBTEST_9(lpNorm_matrix(MatrixXf(0, 0)));
CALL_SUBTEST_9(lpNorm_matrix(MatrixXf(0, 3)));
}