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      1 // Ceres Solver - A fast non-linear least squares minimizer
      2 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
      3 // http://code.google.com/p/ceres-solver/
      4 //
      5 // Redistribution and use in source and binary forms, with or without
      6 // modification, are permitted provided that the following conditions are met:
      7 //
      8 // * Redistributions of source code must retain the above copyright notice,
      9 //   this list of conditions and the following disclaimer.
     10 // * Redistributions in binary form must reproduce the above copyright notice,
     11 //   this list of conditions and the following disclaimer in the documentation
     12 //   and/or other materials provided with the distribution.
     13 // * Neither the name of Google Inc. nor the names of its contributors may be
     14 //   used to endorse or promote products derived from this software without
     15 //   specific prior written permission.
     16 //
     17 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
     18 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
     19 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
     20 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
     21 // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
     22 // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
     23 // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
     24 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
     25 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
     26 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
     27 // POSSIBILITY OF SUCH DAMAGE.
     28 //
     29 // Author: sameeragarwal (at) 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 
     42 namespace ceres {
     43 namespace internal {
     44 
     45 class 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 
     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 
     88     // Unregularized
     89     scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
     90     unregularized_solve_summary =
     91         solver->Solve(transformed_A.get(),
     92                       b_.get(),
     93                       per_solve_options,
     94                       x_unregularized.data());
     95 
     96     // Sparsity structure is changing, reset the solver.
     97     solver.reset(LinearSolver::Create(options));
     98     // Regularized solution
     99     per_solve_options.D = D_.get();
    100     regularized_solve_summary =
    101         solver->Solve(transformed_A.get(),
    102                       b_.get(),
    103                       per_solve_options,
    104                       x_regularized.data());
    105 
    106     EXPECT_EQ(unregularized_solve_summary.termination_type,
    107               LINEAR_SOLVER_SUCCESS);
    108 
    109     for (int i = 0; i < A_->num_cols(); ++i) {
    110       EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8)
    111           << "\nExpected: "
    112           << ConstVectorRef(sol_unregularized_.get(), A_->num_cols()).transpose()
    113           << "\nActual: " << x_unregularized.transpose();
    114     }
    115 
    116     EXPECT_EQ(regularized_solve_summary.termination_type,
    117               LINEAR_SOLVER_SUCCESS);
    118     for (int i = 0; i < A_->num_cols(); ++i) {
    119       EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8)
    120           << "\nExpected: "
    121           << ConstVectorRef(sol_regularized_.get(), A_->num_cols()).transpose()
    122           << "\nActual: " << x_regularized.transpose();
    123     }
    124   }
    125 
    126   scoped_ptr<TripletSparseMatrix> A_;
    127   scoped_array<double> b_;
    128   scoped_array<double> D_;
    129   scoped_array<double> sol_unregularized_;
    130   scoped_array<double> sol_regularized_;
    131 };
    132 
    133 TEST_F(UnsymmetricLinearSolverTest, EigenDenseQR) {
    134   LinearSolver::Options options;
    135   options.type = DENSE_QR;
    136   options.dense_linear_algebra_library_type = EIGEN;
    137   TestSolver(options);
    138 }
    139 
    140 TEST_F(UnsymmetricLinearSolverTest, EigenDenseNormalCholesky) {
    141   LinearSolver::Options options;
    142   options.dense_linear_algebra_library_type = EIGEN;
    143   options.type = DENSE_NORMAL_CHOLESKY;
    144   TestSolver(options);
    145 }
    146 
    147 #ifndef CERES_NO_LAPACK
    148 TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseQR) {
    149   LinearSolver::Options options;
    150   options.type = DENSE_QR;
    151   options.dense_linear_algebra_library_type = LAPACK;
    152   TestSolver(options);
    153 }
    154 
    155 TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseNormalCholesky) {
    156   LinearSolver::Options options;
    157   options.dense_linear_algebra_library_type = LAPACK;
    158   options.type = DENSE_NORMAL_CHOLESKY;
    159   TestSolver(options);
    160 }
    161 #endif
    162 
    163 #ifndef CERES_NO_SUITESPARSE
    164 TEST_F(UnsymmetricLinearSolverTest,
    165        SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
    166   LinearSolver::Options options;
    167   options.sparse_linear_algebra_library_type = SUITE_SPARSE;
    168   options.type = SPARSE_NORMAL_CHOLESKY;
    169   options.use_postordering = false;
    170   TestSolver(options);
    171 }
    172 
    173 TEST_F(UnsymmetricLinearSolverTest,
    174        SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
    175   LinearSolver::Options options;
    176   options.sparse_linear_algebra_library_type = SUITE_SPARSE;
    177   options.type = SPARSE_NORMAL_CHOLESKY;
    178   options.use_postordering = true;
    179   TestSolver(options);
    180 }
    181 
    182 TEST_F(UnsymmetricLinearSolverTest,
    183        SparseNormalCholeskyUsingSuiteSparseDynamicSparsity) {
    184   LinearSolver::Options options;
    185   options.sparse_linear_algebra_library_type = SUITE_SPARSE;
    186   options.type = SPARSE_NORMAL_CHOLESKY;
    187   options.dynamic_sparsity = true;
    188   TestSolver(options);
    189 }
    190 #endif
    191 
    192 #ifndef CERES_NO_CXSPARSE
    193 TEST_F(UnsymmetricLinearSolverTest,
    194        SparseNormalCholeskyUsingCXSparsePreOrdering) {
    195   LinearSolver::Options options;
    196   options.sparse_linear_algebra_library_type = CX_SPARSE;
    197   options.type = SPARSE_NORMAL_CHOLESKY;
    198   options.use_postordering = false;
    199   TestSolver(options);
    200 }
    201 
    202 TEST_F(UnsymmetricLinearSolverTest,
    203        SparseNormalCholeskyUsingCXSparsePostOrdering) {
    204   LinearSolver::Options options;
    205   options.sparse_linear_algebra_library_type = CX_SPARSE;
    206   options.type = SPARSE_NORMAL_CHOLESKY;
    207   options.use_postordering = true;
    208   TestSolver(options);
    209 }
    210 
    211 TEST_F(UnsymmetricLinearSolverTest,
    212        SparseNormalCholeskyUsingCXSparseDynamicSparsity) {
    213   LinearSolver::Options options;
    214   options.sparse_linear_algebra_library_type = CX_SPARSE;
    215   options.type = SPARSE_NORMAL_CHOLESKY;
    216   options.dynamic_sparsity = true;
    217   TestSolver(options);
    218 }
    219 #endif
    220 
    221 #ifdef CERES_USE_EIGEN_SPARSE
    222 TEST_F(UnsymmetricLinearSolverTest,
    223        SparseNormalCholeskyUsingEigenPreOrdering) {
    224   LinearSolver::Options options;
    225   options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
    226   options.type = SPARSE_NORMAL_CHOLESKY;
    227   options.use_postordering = false;
    228   TestSolver(options);
    229 }
    230 
    231 TEST_F(UnsymmetricLinearSolverTest,
    232        SparseNormalCholeskyUsingEigenPostOrdering) {
    233   LinearSolver::Options options;
    234   options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
    235   options.type = SPARSE_NORMAL_CHOLESKY;
    236   options.use_postordering = true;
    237   TestSolver(options);
    238 }
    239 
    240 TEST_F(UnsymmetricLinearSolverTest,
    241        SparseNormalCholeskyUsingEigenDynamicSparsity) {
    242   LinearSolver::Options options;
    243   options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
    244   options.type = SPARSE_NORMAL_CHOLESKY;
    245   options.dynamic_sparsity = true;
    246   TestSolver(options);
    247 }
    248 
    249 #endif  // CERES_USE_EIGEN_SPARSE
    250 
    251 }  // namespace internal
    252 }  // namespace ceres
    253