initial commit

2026-10-22 09:23:54 +08:00
commit 63f557a39f
4448 changed files with 584487 additions and 0 deletions
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+#define EIGEN_NO_DEBUG_SMALL_PRODUCT_BLOCKS
+#include "sparse_solver.h"
+
+#if defined(DEBUG)
+#undef DEBUG
+#endif
+
+#include <Eigen/AccelerateSupport>
+
+template <typename MatrixType, typename DenseMat>
+int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows = 300, int maxCols = 300) {
+  typedef typename MatrixType::Scalar Scalar;
+  int rows = internal::random<int>(1, maxRows);
+  int cols = internal::random<int>(1, maxCols);
+  double density = (std::max)(8.0 / (rows * cols), 0.01);
+
+  A.resize(rows, cols);
+  dA.resize(rows, cols);
+  initSparse<Scalar>(density, dA, A, ForceNonZeroDiag);
+  A.makeCompressed();
+  return rows;
+}
+
+template <typename MatrixType, typename DenseMat>
+int generate_sparse_square_symmetric_problem(MatrixType& A, DenseMat& dA, int maxSize = 300) {
+  typedef typename MatrixType::Scalar Scalar;
+  int rows = internal::random<int>(1, maxSize);
+  int cols = rows;
+  double density = (std::max)(8.0 / (rows * cols), 0.01);
+
+  A.resize(rows, cols);
+  dA.resize(rows, cols);
+  initSparse<Scalar>(density, dA, A, ForceNonZeroDiag);
+  dA = dA * dA.transpose();
+  A = A * A.transpose();
+  A.makeCompressed();
+  return rows;
+}
+
+template <typename Scalar, typename Solver>
+void test_accelerate_ldlt() {
+  typedef SparseMatrix<Scalar> MatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> DenseVector;
+
+  MatrixType A;
+  Matrix<Scalar, Dynamic, Dynamic> dA;
+
+  generate_sparse_square_symmetric_problem(A, dA);
+
+  DenseVector b = DenseVector::Random(A.rows());
+
+  Solver solver;
+  solver.compute(A);
+
+  if (solver.info() != Success) {
+    std::cerr << "sparse LDLT factorization failed\n";
+    exit(0);
+    return;
+  }
+
+  DenseVector x = solver.solve(b);
+
+  if (solver.info() != Success) {
+    std::cerr << "sparse LDLT factorization failed\n";
+    exit(0);
+    return;
+  }
+
+  // Compare with a dense solver
+  DenseVector refX = dA.ldlt().solve(b);
+  VERIFY((A * x).isApprox(A * refX, test_precision<Scalar>()));
+}
+
+template <typename Scalar, typename Solver>
+void test_accelerate_llt() {
+  typedef SparseMatrix<Scalar> MatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> DenseVector;
+
+  MatrixType A;
+  Matrix<Scalar, Dynamic, Dynamic> dA;
+
+  generate_sparse_square_symmetric_problem(A, dA);
+
+  DenseVector b = DenseVector::Random(A.rows());
+
+  Solver solver;
+  solver.compute(A);
+
+  if (solver.info() != Success) {
+    std::cerr << "sparse LLT factorization failed\n";
+    exit(0);
+    return;
+  }
+
+  DenseVector x = solver.solve(b);
+
+  if (solver.info() != Success) {
+    std::cerr << "sparse LLT factorization failed\n";
+    exit(0);
+    return;
+  }
+
+  // Compare with a dense solver
+  DenseVector refX = dA.llt().solve(b);
+  VERIFY((A * x).isApprox(A * refX, test_precision<Scalar>()));
+}
+
+template <typename Scalar, typename Solver>
+void test_accelerate_qr() {
+  typedef SparseMatrix<Scalar> MatrixType;
+  typedef Matrix<Scalar, Dynamic, 1> DenseVector;
+
+  MatrixType A;
+  Matrix<Scalar, Dynamic, Dynamic> dA;
+
+  generate_sparse_rectangular_problem(A, dA);
+
+  DenseVector b = DenseVector::Random(A.rows());
+
+  Solver solver;
+  solver.compute(A);
+
+  if (solver.info() != Success) {
+    std::cerr << "sparse QR factorization failed\n";
+    exit(0);
+    return;
+  }
+
+  DenseVector x = solver.solve(b);
+
+  if (solver.info() != Success) {
+    std::cerr << "sparse QR factorization failed\n";
+    exit(0);
+    return;
+  }
+
+  // Compare with a dense solver
+  DenseVector refX = dA.colPivHouseholderQr().solve(b);
+  VERIFY((A * x).isApprox(A * refX, test_precision<Scalar>()));
+}
+
+template <typename Scalar>
+void run_tests() {
+  typedef SparseMatrix<Scalar> MatrixType;
+
+  test_accelerate_ldlt<Scalar, AccelerateLDLT<MatrixType, Lower> >();
+  test_accelerate_ldlt<Scalar, AccelerateLDLTUnpivoted<MatrixType, Lower> >();
+  test_accelerate_ldlt<Scalar, AccelerateLDLTSBK<MatrixType, Lower> >();
+  test_accelerate_ldlt<Scalar, AccelerateLDLTTPP<MatrixType, Lower> >();
+
+  test_accelerate_ldlt<Scalar, AccelerateLDLT<MatrixType, Upper> >();
+  test_accelerate_ldlt<Scalar, AccelerateLDLTUnpivoted<MatrixType, Upper> >();
+  test_accelerate_ldlt<Scalar, AccelerateLDLTSBK<MatrixType, Upper> >();
+  test_accelerate_ldlt<Scalar, AccelerateLDLTTPP<MatrixType, Upper> >();
+
+  test_accelerate_llt<Scalar, AccelerateLLT<MatrixType, Lower> >();
+
+  test_accelerate_llt<Scalar, AccelerateLLT<MatrixType, Upper> >();
+
+  test_accelerate_qr<Scalar, AccelerateQR<MatrixType> >();
+}
+
+EIGEN_DECLARE_TEST(accelerate_support) {
+  CALL_SUBTEST_1(run_tests<float>());
+  CALL_SUBTEST_2(run_tests<double>());
+}