| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2015 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
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| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
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| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
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| // |
| // Author: sameeragarwal@google.com (Sameer Agarwal) |
| |
| #include "ceres/partitioned_matrix_view.h" |
| |
| #include <memory> |
| #include <vector> |
| |
| #include "ceres/block_structure.h" |
| #include "ceres/casts.h" |
| #include "ceres/internal/eigen.h" |
| #include "ceres/linear_least_squares_problems.h" |
| #include "ceres/random.h" |
| #include "ceres/sparse_matrix.h" |
| #include "glog/logging.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| const double kEpsilon = 1e-14; |
| |
| class PartitionedMatrixViewTest : public ::testing::Test { |
| protected: |
| void SetUp() final { |
| srand(5); |
| std::unique_ptr<LinearLeastSquaresProblem> problem( |
| CreateLinearLeastSquaresProblemFromId(2)); |
| CHECK(problem != nullptr); |
| A_.reset(problem->A.release()); |
| |
| num_cols_ = A_->num_cols(); |
| num_rows_ = A_->num_rows(); |
| num_eliminate_blocks_ = problem->num_eliminate_blocks; |
| LinearSolver::Options options; |
| options.elimination_groups.push_back(num_eliminate_blocks_); |
| pmv_.reset(PartitionedMatrixViewBase::Create( |
| options, *down_cast<BlockSparseMatrix*>(A_.get()))); |
| } |
| |
| int num_rows_; |
| int num_cols_; |
| int num_eliminate_blocks_; |
| std::unique_ptr<SparseMatrix> A_; |
| std::unique_ptr<PartitionedMatrixViewBase> pmv_; |
| }; |
| |
| TEST_F(PartitionedMatrixViewTest, DimensionsTest) { |
| EXPECT_EQ(pmv_->num_col_blocks_e(), num_eliminate_blocks_); |
| EXPECT_EQ(pmv_->num_col_blocks_f(), num_cols_ - num_eliminate_blocks_); |
| EXPECT_EQ(pmv_->num_cols_e(), num_eliminate_blocks_); |
| EXPECT_EQ(pmv_->num_cols_f(), num_cols_ - num_eliminate_blocks_); |
| EXPECT_EQ(pmv_->num_cols(), A_->num_cols()); |
| EXPECT_EQ(pmv_->num_rows(), A_->num_rows()); |
| } |
| |
| TEST_F(PartitionedMatrixViewTest, RightMultiplyE) { |
| Vector x1(pmv_->num_cols_e()); |
| Vector x2(pmv_->num_cols()); |
| x2.setZero(); |
| |
| for (int i = 0; i < pmv_->num_cols_e(); ++i) { |
| x1(i) = x2(i) = RandDouble(); |
| } |
| |
| Vector y1 = Vector::Zero(pmv_->num_rows()); |
| pmv_->RightMultiplyE(x1.data(), y1.data()); |
| |
| Vector y2 = Vector::Zero(pmv_->num_rows()); |
| A_->RightMultiply(x2.data(), y2.data()); |
| |
| for (int i = 0; i < pmv_->num_rows(); ++i) { |
| EXPECT_NEAR(y1(i), y2(i), kEpsilon); |
| } |
| } |
| |
| TEST_F(PartitionedMatrixViewTest, RightMultiplyF) { |
| Vector x1(pmv_->num_cols_f()); |
| Vector x2 = Vector::Zero(pmv_->num_cols()); |
| |
| for (int i = 0; i < pmv_->num_cols_f(); ++i) { |
| x1(i) = RandDouble(); |
| x2(i + pmv_->num_cols_e()) = x1(i); |
| } |
| |
| Vector y1 = Vector::Zero(pmv_->num_rows()); |
| pmv_->RightMultiplyF(x1.data(), y1.data()); |
| |
| Vector y2 = Vector::Zero(pmv_->num_rows()); |
| A_->RightMultiply(x2.data(), y2.data()); |
| |
| for (int i = 0; i < pmv_->num_rows(); ++i) { |
| EXPECT_NEAR(y1(i), y2(i), kEpsilon); |
| } |
| } |
| |
| TEST_F(PartitionedMatrixViewTest, LeftMultiply) { |
| Vector x = Vector::Zero(pmv_->num_rows()); |
| for (int i = 0; i < pmv_->num_rows(); ++i) { |
| x(i) = RandDouble(); |
| } |
| |
| Vector y = Vector::Zero(pmv_->num_cols()); |
| Vector y1 = Vector::Zero(pmv_->num_cols_e()); |
| Vector y2 = Vector::Zero(pmv_->num_cols_f()); |
| |
| A_->LeftMultiply(x.data(), y.data()); |
| pmv_->LeftMultiplyE(x.data(), y1.data()); |
| pmv_->LeftMultiplyF(x.data(), y2.data()); |
| |
| for (int i = 0; i < pmv_->num_cols(); ++i) { |
| EXPECT_NEAR(y(i), |
| (i < pmv_->num_cols_e()) ? y1(i) : y2(i - pmv_->num_cols_e()), |
| kEpsilon); |
| } |
| } |
| |
| TEST_F(PartitionedMatrixViewTest, BlockDiagonalEtE) { |
| std::unique_ptr<BlockSparseMatrix> block_diagonal_ee( |
| pmv_->CreateBlockDiagonalEtE()); |
| const CompressedRowBlockStructure* bs = block_diagonal_ee->block_structure(); |
| |
| EXPECT_EQ(block_diagonal_ee->num_rows(), 2); |
| EXPECT_EQ(block_diagonal_ee->num_cols(), 2); |
| EXPECT_EQ(bs->cols.size(), 2); |
| EXPECT_EQ(bs->rows.size(), 2); |
| |
| EXPECT_NEAR(block_diagonal_ee->values()[0], 10.0, kEpsilon); |
| EXPECT_NEAR(block_diagonal_ee->values()[1], 155.0, kEpsilon); |
| } |
| |
| TEST_F(PartitionedMatrixViewTest, BlockDiagonalFtF) { |
| std::unique_ptr<BlockSparseMatrix> block_diagonal_ff( |
| pmv_->CreateBlockDiagonalFtF()); |
| const CompressedRowBlockStructure* bs = block_diagonal_ff->block_structure(); |
| |
| EXPECT_EQ(block_diagonal_ff->num_rows(), 3); |
| EXPECT_EQ(block_diagonal_ff->num_cols(), 3); |
| EXPECT_EQ(bs->cols.size(), 3); |
| EXPECT_EQ(bs->rows.size(), 3); |
| EXPECT_NEAR(block_diagonal_ff->values()[0], 70.0, kEpsilon); |
| EXPECT_NEAR(block_diagonal_ff->values()[1], 17.0, kEpsilon); |
| EXPECT_NEAR(block_diagonal_ff->values()[2], 37.0, kEpsilon); |
| } |
| |
| } // namespace internal |
| } // namespace ceres |