unsymmetric_linear_solver_test.cc revision 399f7d09e0c45af54b77b4ab9508d6f23759b927 
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(const LinearSolver::Options& options) {
60    scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
61
62    LinearSolver::PerSolveOptions per_solve_options;
63    LinearSolver::Summary unregularized_solve_summary;
64    LinearSolver::Summary regularized_solve_summary;
65    Vector x_unregularized(A_->num_cols());
66    Vector x_regularized(A_->num_cols());
67
68    scoped_ptr<SparseMatrix> transformed_A;
69
70    if (options.type == DENSE_QR ||
71        options.type == DENSE_NORMAL_CHOLESKY) {
72      transformed_A.reset(new DenseSparseMatrix(*A_));
73    } else if (options.type == SPARSE_NORMAL_CHOLESKY) {
74      CompressedRowSparseMatrix* crsm =  new CompressedRowSparseMatrix(*A_);
75      // Add row/column blocks structure.
76      for (int i = 0; i < A_->num_rows(); ++i) {
77        crsm->mutable_row_blocks()->push_back(1);
78      }
79
80      for (int i = 0; i < A_->num_cols(); ++i) {
81        crsm->mutable_col_blocks()->push_back(1);
82      }
83      transformed_A.reset(crsm);
84    } else {
85      LOG(FATAL) << "Unknown linear solver : " << options.type;
86    }
87    // Unregularized
88    unregularized_solve_summary =
89        solver->Solve(transformed_A.get(),
90                      b_.get(),
91                      per_solve_options,
92                      x_unregularized.data());
93
94    // Regularized solution
95    per_solve_options.D = D_.get();
96    regularized_solve_summary =
97        solver->Solve(transformed_A.get(),
98                      b_.get(),
99                      per_solve_options,
100                      x_regularized.data());
101
102    EXPECT_EQ(unregularized_solve_summary.termination_type, TOLERANCE);
103
104    for (int i = 0; i < A_->num_cols(); ++i) {
105      EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8);
106    }
107
108    EXPECT_EQ(regularized_solve_summary.termination_type, TOLERANCE);
109    for (int i = 0; i < A_->num_cols(); ++i) {
110      EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8);
111    }
112  }
113
114  scoped_ptr<TripletSparseMatrix> A_;
115  scoped_array<double> b_;
116  scoped_array<double> D_;
117  scoped_array<double> sol_unregularized_;
118  scoped_array<double> sol_regularized_;
119};
120
121TEST_F(UnsymmetricLinearSolverTest, EigenDenseQR) {
122  LinearSolver::Options options;
123  options.type = DENSE_QR;
124  options.dense_linear_algebra_library_type = EIGEN;
125  TestSolver(options);
126}
127
128TEST_F(UnsymmetricLinearSolverTest, EigenDenseNormalCholesky) {
129  LinearSolver::Options options;
130  options.dense_linear_algebra_library_type = EIGEN;
131  options.type = DENSE_NORMAL_CHOLESKY;
132  TestSolver(options);
133}
134
135#ifndef CERES_NO_LAPACK
136TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseQR) {
137  LinearSolver::Options options;
138  options.type = DENSE_QR;
139  options.dense_linear_algebra_library_type = LAPACK;
140  TestSolver(options);
141}
142
143TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseNormalCholesky) {
144  LinearSolver::Options options;
145  options.dense_linear_algebra_library_type = LAPACK;
146  options.type = DENSE_NORMAL_CHOLESKY;
147  TestSolver(options);
148}
149#endif
150
151#ifndef CERES_NO_SUITESPARSE
152TEST_F(UnsymmetricLinearSolverTest,
153       SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
154  LinearSolver::Options options;
155  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
156  options.type = SPARSE_NORMAL_CHOLESKY;
157  options.use_postordering = false;
158  TestSolver(options);
159}
160
161TEST_F(UnsymmetricLinearSolverTest,
162       SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
163  LinearSolver::Options options;
164  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
165  options.type = SPARSE_NORMAL_CHOLESKY;
166  options.use_postordering = true;
167  TestSolver(options);
168}
169#endif
170
171#ifndef CERES_NO_CXSPARSE
172TEST_F(UnsymmetricLinearSolverTest,
173       SparseNormalCholeskyUsingCXSparsePreOrdering) {
174  LinearSolver::Options options;
175  options.sparse_linear_algebra_library_type = CX_SPARSE;
176  options.type = SPARSE_NORMAL_CHOLESKY;
177  options.use_postordering = false;
178  TestSolver(options);
179}
180
181TEST_F(UnsymmetricLinearSolverTest,
182       SparseNormalCholeskyUsingCXSparsePostOrdering) {
183  LinearSolver::Options options;
184  options.sparse_linear_algebra_library_type = CX_SPARSE;
185  options.type = SPARSE_NORMAL_CHOLESKY;
186  options.use_postordering = true;
187  TestSolver(options);
188}
189#endif
190
191}  // namespace internal
192}  // namespace ceres
193