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FESADev/tests/unit/math/sparse_matrix_test.cpp
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#include "fesa/fem/dof_manager.hpp"
#include "fesa/math/matrix.hpp"
#include "fesa/math/sparse_matrix.hpp"
#include <gtest/gtest.h>
#include <algorithm>
#include <limits>
#include <type_traits>
#include <utility>
#include <vector>
namespace {
using fesa::CooContribution;
using fesa::SparseMatrix;
using fesa::SparsePattern;
using fesa::Vector;
static_assert(
!std::is_base_of_v<fesa::Matrix, SparseMatrix>,
"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<CooContribution> 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<double>{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<void>(matrix.multiply(Vector{3U})), std::invalid_argument);
}
TEST(SparseAssembly, ReducesDuplicatesInFixedTupleOrder) {
const SparsePattern pattern{{0U, 1U}, {0U}};
const std::vector<CooContribution> 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<CooContribution> 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<double>::infinity)(), 0U, 0U}},
oneEntry);
expectFailure(
1U,
1U,
{{0U, 0U, (std::numeric_limits<double>::quiet_NaN)(), 0U, 0U}},
oneEntry);
expectFailure(
1U,
1U,
{{0U, 0U, (std::numeric_limits<double>::max)(), 0U, 0U},
{0U, 0U, (std::numeric_limits<double>::max)(), 1U, 0U}},
oneEntry);
}
TEST(SparseAssembly, PreservesExpectedStructuralZeros) {
const SparsePattern pattern{
{0U, 2U, 4U, 5U},
{0U, 2U, 1U, 2U, 0U}};
std::vector<CooContribution> 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<double>{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