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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
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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_ = std::move(problem->A);
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_ = 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