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/partitioned_matrix_view.h" 32 33 #include <vector> 34 #include "ceres/block_structure.h" 35 #include "ceres/casts.h" 36 #include "ceres/internal/eigen.h" 37 #include "ceres/internal/scoped_ptr.h" 38 #include "ceres/linear_least_squares_problems.h" 39 #include "ceres/random.h" 40 #include "ceres/sparse_matrix.h" 41 #include "glog/logging.h" 42 #include "gtest/gtest.h" 43 44 namespace ceres { 45 namespace internal { 46 47 const double kEpsilon = 1e-14; 48 49 class PartitionedMatrixViewTest : public ::testing::Test { 50 protected : 51 virtual void SetUp() { 52 srand(5); 53 scoped_ptr<LinearLeastSquaresProblem> problem( 54 CreateLinearLeastSquaresProblemFromId(2)); 55 CHECK_NOTNULL(problem.get()); 56 A_.reset(problem->A.release()); 57 58 num_cols_ = A_->num_cols(); 59 num_rows_ = A_->num_rows(); 60 num_eliminate_blocks_ = problem->num_eliminate_blocks; 61 LinearSolver::Options options; 62 options.elimination_groups.push_back(num_eliminate_blocks_); 63 pmv_.reset(PartitionedMatrixViewBase::Create( 64 options, 65 *down_cast<BlockSparseMatrix*>(A_.get()))); 66 } 67 68 int num_rows_; 69 int num_cols_; 70 int num_eliminate_blocks_; 71 scoped_ptr<SparseMatrix> A_; 72 scoped_ptr<PartitionedMatrixViewBase> pmv_; 73 }; 74 75 TEST_F(PartitionedMatrixViewTest, DimensionsTest) { 76 EXPECT_EQ(pmv_->num_col_blocks_e(), num_eliminate_blocks_); 77 EXPECT_EQ(pmv_->num_col_blocks_f(), num_cols_ - num_eliminate_blocks_); 78 EXPECT_EQ(pmv_->num_cols_e(), num_eliminate_blocks_); 79 EXPECT_EQ(pmv_->num_cols_f(), num_cols_ - num_eliminate_blocks_); 80 EXPECT_EQ(pmv_->num_cols(), A_->num_cols()); 81 EXPECT_EQ(pmv_->num_rows(), A_->num_rows()); 82 } 83 84 TEST_F(PartitionedMatrixViewTest, RightMultiplyE) { 85 Vector x1(pmv_->num_cols_e()); 86 Vector x2(pmv_->num_cols()); 87 x2.setZero(); 88 89 for (int i = 0; i < pmv_->num_cols_e(); ++i) { 90 x1(i) = x2(i) = RandDouble(); 91 } 92 93 Vector y1 = Vector::Zero(pmv_->num_rows()); 94 pmv_->RightMultiplyE(x1.data(), y1.data()); 95 96 Vector y2 = Vector::Zero(pmv_->num_rows()); 97 A_->RightMultiply(x2.data(), y2.data()); 98 99 for (int i = 0; i < pmv_->num_rows(); ++i) { 100 EXPECT_NEAR(y1(i), y2(i), kEpsilon); 101 } 102 } 103 104 TEST_F(PartitionedMatrixViewTest, RightMultiplyF) { 105 Vector x1(pmv_->num_cols_f()); 106 Vector x2 = Vector::Zero(pmv_->num_cols()); 107 108 for (int i = 0; i < pmv_->num_cols_f(); ++i) { 109 x1(i) = RandDouble(); 110 x2(i + pmv_->num_cols_e()) = x1(i); 111 } 112 113 Vector y1 = Vector::Zero(pmv_->num_rows()); 114 pmv_->RightMultiplyF(x1.data(), y1.data()); 115 116 Vector y2 = Vector::Zero(pmv_->num_rows()); 117 A_->RightMultiply(x2.data(), y2.data()); 118 119 for (int i = 0; i < pmv_->num_rows(); ++i) { 120 EXPECT_NEAR(y1(i), y2(i), kEpsilon); 121 } 122 } 123 124 TEST_F(PartitionedMatrixViewTest, LeftMultiply) { 125 Vector x = Vector::Zero(pmv_->num_rows()); 126 for (int i = 0; i < pmv_->num_rows(); ++i) { 127 x(i) = RandDouble(); 128 } 129 130 Vector y = Vector::Zero(pmv_->num_cols()); 131 Vector y1 = Vector::Zero(pmv_->num_cols_e()); 132 Vector y2 = Vector::Zero(pmv_->num_cols_f()); 133 134 A_->LeftMultiply(x.data(), y.data()); 135 pmv_->LeftMultiplyE(x.data(), y1.data()); 136 pmv_->LeftMultiplyF(x.data(), y2.data()); 137 138 for (int i = 0; i < pmv_->num_cols(); ++i) { 139 EXPECT_NEAR(y(i), 140 (i < pmv_->num_cols_e()) ? y1(i) : y2(i - pmv_->num_cols_e()), 141 kEpsilon); 142 } 143 } 144 145 TEST_F(PartitionedMatrixViewTest, BlockDiagonalEtE) { 146 scoped_ptr<BlockSparseMatrix> 147 block_diagonal_ee(pmv_->CreateBlockDiagonalEtE()); 148 const CompressedRowBlockStructure* bs = block_diagonal_ee->block_structure(); 149 150 EXPECT_EQ(block_diagonal_ee->num_rows(), 2); 151 EXPECT_EQ(block_diagonal_ee->num_cols(), 2); 152 EXPECT_EQ(bs->cols.size(), 2); 153 EXPECT_EQ(bs->rows.size(), 2); 154 155 EXPECT_NEAR(block_diagonal_ee->values()[0], 10.0, kEpsilon); 156 EXPECT_NEAR(block_diagonal_ee->values()[1], 155.0, kEpsilon); 157 } 158 159 TEST_F(PartitionedMatrixViewTest, BlockDiagonalFtF) { 160 scoped_ptr<BlockSparseMatrix> 161 block_diagonal_ff(pmv_->CreateBlockDiagonalFtF()); 162 const CompressedRowBlockStructure* bs = block_diagonal_ff->block_structure(); 163 164 EXPECT_EQ(block_diagonal_ff->num_rows(), 3); 165 EXPECT_EQ(block_diagonal_ff->num_cols(), 3); 166 EXPECT_EQ(bs->cols.size(), 3); 167 EXPECT_EQ(bs->rows.size(), 3); 168 EXPECT_NEAR(block_diagonal_ff->values()[0], 70.0, kEpsilon); 169 EXPECT_NEAR(block_diagonal_ff->values()[1], 17.0, kEpsilon); 170 EXPECT_NEAR(block_diagonal_ff->values()[2], 37.0, kEpsilon); 171 } 172 173 } // namespace internal 174 } // namespace ceres 175
