unsymmetric_linear_solver_test.cc revision 0ae28bd5885b5daa526898fcf7c323dc2c3e1963 
1// Ceres Solver - A fast non-linear least squares minimizer
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29// Author: sameeragarwal@google.com (Sameer Agarwal)
30
31#include "ceres/casts.h"
32#include "ceres/compressed_row_sparse_matrix.h"
33#include "ceres/internal/scoped_ptr.h"
34#include "ceres/linear_least_squares_problems.h"
35#include "ceres/linear_solver.h"
36#include "ceres/triplet_sparse_matrix.h"
37#include "ceres/types.h"
38#include "glog/logging.h"
39#include "gtest/gtest.h"
40
41
42namespace ceres {
43namespace internal {
44
45class UnsymmetricLinearSolverTest : public ::testing::Test {
46 protected :
47  virtual void SetUp() {
48    scoped_ptr<LinearLeastSquaresProblem> problem(
49        CreateLinearLeastSquaresProblemFromId(0));
50
51    CHECK_NOTNULL(problem.get());
52    A_.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
53    b_.reset(problem->b.release());
54    D_.reset(problem->D.release());
55    sol_unregularized_.reset(problem->x.release());
56    sol_regularized_.reset(problem->x_D.release());
57  }
58
59  void TestSolver(
60      LinearSolverType linear_solver_type,
61      SparseLinearAlgebraLibraryType sparse_linear_algebra_library) {
62    LinearSolver::Options options;
63    options.type = linear_solver_type;
64    options.sparse_linear_algebra_library = sparse_linear_algebra_library;
65    options.use_block_amd = false;
66    scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
67
68    LinearSolver::PerSolveOptions per_solve_options;
69    LinearSolver::Summary unregularized_solve_summary;
70    LinearSolver::Summary regularized_solve_summary;
71    Vector x_unregularized(A_->num_cols());
72    Vector x_regularized(A_->num_cols());
73
74    scoped_ptr<SparseMatrix> transformed_A;
75
76    if (linear_solver_type == DENSE_QR ||
77        linear_solver_type == DENSE_NORMAL_CHOLESKY) {
78      transformed_A.reset(new DenseSparseMatrix(*A_));
79    } else if (linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
80      transformed_A.reset(new   CompressedRowSparseMatrix(*A_));
81    } else {
82      LOG(FATAL) << "Unknown linear solver : " << linear_solver_type;
83    }
84    // Unregularized
85    unregularized_solve_summary =
86        solver->Solve(transformed_A.get(),
87                      b_.get(),
88                      per_solve_options,
89                      x_unregularized.data());
90
91    // Regularized solution
92    per_solve_options.D = D_.get();
93    regularized_solve_summary =
94        solver->Solve(transformed_A.get(),
95                      b_.get(),
96                      per_solve_options,
97                      x_regularized.data());
98
99    EXPECT_EQ(unregularized_solve_summary.termination_type, TOLERANCE);
100
101    for (int i = 0; i < A_->num_cols(); ++i) {
102      EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8);
103    }
104
105    EXPECT_EQ(regularized_solve_summary.termination_type, TOLERANCE);
106    for (int i = 0; i < A_->num_cols(); ++i) {
107      EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8);
108    }
109  }
110
111  scoped_ptr<TripletSparseMatrix> A_;
112  scoped_array<double> b_;
113  scoped_array<double> D_;
114  scoped_array<double> sol_unregularized_;
115  scoped_array<double> sol_regularized_;
116};
117
118TEST_F(UnsymmetricLinearSolverTest, DenseQR) {
119  TestSolver(DENSE_QR, SUITE_SPARSE);
120}
121
122TEST_F(UnsymmetricLinearSolverTest, DenseNormalCholesky) {
123  TestSolver(DENSE_NORMAL_CHOLESKY, SUITE_SPARSE);
124}
125
126#ifndef CERES_NO_SUITESPARSE
127TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingSuiteSparse) {
128  TestSolver(SPARSE_NORMAL_CHOLESKY, SUITE_SPARSE);
129}
130#endif
131
132#ifndef CERES_NO_CXSPARSE
133TEST_F(UnsymmetricLinearSolverTest, SparseNormalCholeskyUsingCXSparse) {
134  TestSolver(SPARSE_NORMAL_CHOLESKY, CX_SPARSE);
135}
136#endif
137
138}  // namespace internal
139}  // namespace ceres
140