#include "fesa/fem/dof_manager.hpp" #include "fesa/math/matrix.hpp" #include "fesa/math/sparse_matrix.hpp" #include #include #include #include #include #include namespace { using fesa::CooContribution; using fesa::SparseMatrix; using fesa::SparsePattern; using fesa::Vector; static_assert( !std::is_base_of_v, "SparseMatrix must own CSR storage independently of dense Matrix."); TEST(SparseAssembly, ValidatesKnownCsrAndMultiply) { const SparsePattern pattern{ {0U, 2U, 2U, 4U, 5U}, {0U, 2U, 1U, 3U, 3U}}; std::vector contributions{ {2U, 3U, 4.0, 2U, 0U}, {0U, 2U, 2.0, 0U, 1U}, {3U, 3U, 5.0, 3U, 0U}, {0U, 0U, 1.0, 0U, 0U}, {2U, 1U, 3.0, 1U, 0U}}; auto result = SparseMatrix::fromCoo( 4U, 4U, std::move(contributions), pattern); ASSERT_TRUE(result.hasValue()); const SparseMatrix& matrix = result.value(); EXPECT_EQ(matrix.rows(), 4U); EXPECT_EQ(matrix.columns(), 4U); EXPECT_EQ(matrix.rowOffsets(), pattern.rowOffsets); EXPECT_EQ(matrix.columnIndices(), pattern.columnIndices); EXPECT_EQ(matrix.values(), (std::vector{1.0, 2.0, 3.0, 4.0, 5.0})); EXPECT_TRUE(matrix.validate().isOk()); Vector rhs{4U}; rhs[0U] = 1.0; rhs[1U] = 2.0; rhs[2U] = 3.0; rhs[3U] = 4.0; const Vector product = matrix.multiply(rhs); ASSERT_EQ(product.size(), 4U); EXPECT_DOUBLE_EQ(product[0U], 7.0); EXPECT_DOUBLE_EQ(product[1U], 0.0); EXPECT_DOUBLE_EQ(product[2U], 22.0); EXPECT_DOUBLE_EQ(product[3U], 20.0); EXPECT_THROW(static_cast(matrix.multiply(Vector{3U})), std::invalid_argument); } TEST(SparseAssembly, ReducesDuplicatesInFixedTupleOrder) { const SparsePattern pattern{{0U, 1U}, {0U}}; const std::vector contributions{ {0U, 0U, 1.0, 2U, 0U}, {0U, 0U, -1.0e16, 1U, 0U}, {0U, 0U, 1.0e16, 0U, 0U}}; auto first = SparseMatrix::fromCoo(1U, 1U, contributions, pattern); ASSERT_TRUE(first.hasValue()); ASSERT_EQ(first.value().values().size(), 1U); EXPECT_DOUBLE_EQ(first.value().values()[0U], 1.0); auto reversedContributions = contributions; std::reverse(reversedContributions.begin(), reversedContributions.end()); auto second = SparseMatrix::fromCoo( 1U, 1U, std::move(reversedContributions), pattern); ASSERT_TRUE(second.hasValue()); EXPECT_EQ(second.value().rowOffsets(), first.value().rowOffsets()); EXPECT_EQ(second.value().columnIndices(), first.value().columnIndices()); EXPECT_EQ(second.value().values(), first.value().values()); } TEST(SparseAssembly, RejectsInvalidIndexPatternAndShape) { const SparsePattern oneEntry{{0U, 1U}, {0U}}; const auto expectFailure = []( std::size_t rows, std::size_t columns, std::vector contributions, const SparsePattern& pattern) { auto result = SparseMatrix::fromCoo( rows, columns, std::move(contributions), pattern); EXPECT_FALSE(result.hasValue()); if (!result.hasValue()) { EXPECT_FALSE(result.status().isOk()); EXPECT_EQ(result.status().failureCategory(), fesa::FailureCategory::model); EXPECT_FALSE(result.status().diagnostics().empty()); } }; expectFailure(2U, 2U, {}, {{0U, 0U}, {}}); expectFailure(1U, 1U, {}, {{1U, 1U}, {0U}}); expectFailure(2U, 2U, {}, {{0U, 1U, 0U}, {0U}}); expectFailure(1U, 2U, {}, {{0U, 2U}, {1U, 0U}}); expectFailure(1U, 1U, {}, {{0U, 2U}, {0U, 0U}}); expectFailure(1U, 1U, {}, {{0U, 1U}, {1U}}); expectFailure(1U, 1U, {{1U, 0U, 1.0, 0U, 0U}}, oneEntry); expectFailure(1U, 1U, {{0U, 1U, 1.0, 0U, 0U}}, oneEntry); expectFailure(1U, 2U, {{0U, 1U, 1.0, 0U, 0U}}, {{0U, 1U}, {0U}}); expectFailure( 1U, 1U, {{0U, 0U, (std::numeric_limits::infinity)(), 0U, 0U}}, oneEntry); expectFailure( 1U, 1U, {{0U, 0U, (std::numeric_limits::quiet_NaN)(), 0U, 0U}}, oneEntry); expectFailure( 1U, 1U, {{0U, 0U, (std::numeric_limits::max)(), 0U, 0U}, {0U, 0U, (std::numeric_limits::max)(), 1U, 0U}}, oneEntry); } TEST(SparseAssembly, PreservesExpectedStructuralZeros) { const SparsePattern pattern{ {0U, 2U, 4U, 5U}, {0U, 2U, 1U, 2U, 0U}}; std::vector contributions{ {0U, 0U, 2.0, 0U, 0U}, {1U, 1U, 4.0, 0U, 1U}, {1U, 1U, -4.0, 1U, 0U}}; auto result = SparseMatrix::fromCoo( 3U, 3U, std::move(contributions), pattern); ASSERT_TRUE(result.hasValue()); EXPECT_EQ(result.value().rowOffsets(), pattern.rowOffsets); EXPECT_EQ(result.value().columnIndices(), pattern.columnIndices); EXPECT_EQ( result.value().values(), (std::vector{2.0, 0.0, 0.0, 0.0, 0.0})); EXPECT_EQ( std::count(result.value().values().begin(), result.value().values().end(), 0.0), 4); } } // namespace