| // This file is part of Eigen, a lightweight C++ template library | |
| // for linear algebra. | |
| // | |
| // Copyright (C) 2011 Gael Guennebaud <g.gael@free.fr> | |
| // | |
| // This Source Code Form is subject to the terms of the Mozilla | |
| // Public License v. 2.0. If a copy of the MPL was not distributed | |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. | |
| // SPDX-License-Identifier: MPL-2.0 | |
| #include "sparse_solver.h" | |
| #include <Eigen/IterativeLinearSolvers> | |
| template <typename T, typename I_> | |
| void test_conjugate_gradient_T() { | |
| typedef SparseMatrix<T, 0, I_> SparseMatrixType; | |
| ConjugateGradient<SparseMatrixType, Lower> cg_colmajor_lower_diag; | |
| ConjugateGradient<SparseMatrixType, Upper> cg_colmajor_upper_diag; | |
| ConjugateGradient<SparseMatrixType, Lower | Upper> cg_colmajor_loup_diag; | |
| ConjugateGradient<SparseMatrixType, Lower, IdentityPreconditioner> cg_colmajor_lower_I; | |
| ConjugateGradient<SparseMatrixType, Upper, IdentityPreconditioner> cg_colmajor_upper_I; | |
| CALL_SUBTEST(check_sparse_spd_solving(cg_colmajor_lower_diag)); | |
| CALL_SUBTEST(check_sparse_spd_solving(cg_colmajor_upper_diag)); | |
| CALL_SUBTEST(check_sparse_spd_solving(cg_colmajor_loup_diag)); | |
| CALL_SUBTEST(check_sparse_spd_solving(cg_colmajor_lower_I)); | |
| CALL_SUBTEST(check_sparse_spd_solving(cg_colmajor_upper_I)); | |
| } | |
| // Regression for issue #1704: default-constructing an iterative solver | |
| // templated on a fixed-size MatrixType tripped a resize(0,0) size assertion. | |
| template <typename MatrixType> | |
| void test_default_construct_fixed_size() { | |
| ConjugateGradient<MatrixType> cg; | |
| BiCGSTAB<MatrixType> bicg; | |
| LeastSquaresConjugateGradient<MatrixType> lscg; | |
| } | |
| void test_conjugate_gradient_extreme_rhs() { | |
| const Matrix2d mat = Matrix2d::Identity(); | |
| const Vector2d direction = (Vector2d() << 1, -1).finished(); | |
| ConjugateGradient<Matrix2d, Lower | Upper, IdentityPreconditioner> solver(mat); | |
| solver.setTolerance(1e-12); | |
| for (double scale : {1e-200, 1e200}) { | |
| const Vector2d rhs = scale * direction; | |
| const Vector2d guess = 0.5 * rhs; | |
| Vector2d x = solver.solve(rhs); | |
| VERIFY_IS_EQUAL(solver.info(), Success); | |
| VERIFY(x.allFinite()); | |
| VERIFY_IS_APPROX(x / scale, direction); | |
| x = solver.solveWithGuess(rhs, guess); | |
| VERIFY_IS_EQUAL(solver.info(), Success); | |
| VERIFY(x.allFinite()); | |
| VERIFY_IS_APPROX(x / scale, direction); | |
| } | |
| } | |
| EIGEN_DECLARE_TEST(conjugate_gradient) { | |
| CALL_SUBTEST_1((test_conjugate_gradient_T<double, int>())); | |
| CALL_SUBTEST_2((test_conjugate_gradient_T<std::complex<double>, int>())); | |
| CALL_SUBTEST_3((test_conjugate_gradient_T<double, long int>())); | |
| CALL_SUBTEST_4(test_default_construct_fixed_size<Matrix3d>()); | |
| CALL_SUBTEST_5(test_conjugate_gradient_extreme_rhs()); | |
| } |