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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:
//
// * Redistributions of source code must retain the above copyright notice,
// 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
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_random_access_diagonal_matrix.h"
#include <limits>
#include <memory>
#include <vector>
#include "Eigen/Cholesky"
#include "ceres/internal/eigen.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres::internal {
class BlockRandomAccessDiagonalMatrixTest : public ::testing::Test {
public:
void SetUp() override {
std::vector<Block> blocks;
blocks.emplace_back(3, 0);
blocks.emplace_back(4, 3);
blocks.emplace_back(5, 7);
const int num_rows = 3 + 4 + 5;
num_nonzeros_ = 3 * 3 + 4 * 4 + 5 * 5;
m_ =
std::make_unique<BlockRandomAccessDiagonalMatrix>(blocks, &context_, 1);
EXPECT_EQ(m_->num_rows(), num_rows);
EXPECT_EQ(m_->num_cols(), num_rows);
for (int i = 0; i < blocks.size(); ++i) {
const int row_block_id = i;
int col_block_id;
int row;
int col;
int row_stride;
int col_stride;
for (int j = 0; j < blocks.size(); ++j) {
col_block_id = j;
CellInfo* cell = m_->GetCell(
row_block_id, col_block_id, &row, &col, &row_stride, &col_stride);
// Off diagonal entries are not present.
if (i != j) {
EXPECT_TRUE(cell == nullptr);
continue;
}
EXPECT_TRUE(cell != nullptr);
EXPECT_EQ(row, 0);
EXPECT_EQ(col, 0);
EXPECT_EQ(row_stride, blocks[row_block_id].size);
EXPECT_EQ(col_stride, blocks[col_block_id].size);
// Write into the block
MatrixRef(cell->values, row_stride, col_stride)
.block(row,
col,
blocks[row_block_id].size,
blocks[col_block_id].size) =
(row_block_id + 1) * (col_block_id + 1) *
Matrix::Ones(blocks[row_block_id].size,
blocks[col_block_id].size) +
Matrix::Identity(blocks[row_block_id].size,
blocks[row_block_id].size);
}
}
}
protected:
ContextImpl context_;
int num_nonzeros_;
std::unique_ptr<BlockRandomAccessDiagonalMatrix> m_;
};
TEST_F(BlockRandomAccessDiagonalMatrixTest, MatrixContents) {
auto* crsm = m_->matrix();
EXPECT_EQ(crsm->num_nonzeros(), num_nonzeros_);
Matrix dense;
crsm->ToDenseMatrix(&dense);
double kTolerance = 1e-14;
// (0,0)
EXPECT_NEAR(
(dense.block(0, 0, 3, 3) - (Matrix::Ones(3, 3) + Matrix::Identity(3, 3)))
.norm(),
0.0,
kTolerance);
// (1,1)
EXPECT_NEAR((dense.block(3, 3, 4, 4) -
(2 * 2 * Matrix::Ones(4, 4) + Matrix::Identity(4, 4)))
.norm(),
0.0,
kTolerance);
// (1,1)
EXPECT_NEAR((dense.block(7, 7, 5, 5) -
(3 * 3 * Matrix::Ones(5, 5) + Matrix::Identity(5, 5)))
.norm(),
0.0,
kTolerance);
// There is nothing else in the matrix besides these four blocks.
EXPECT_NEAR(
dense.norm(),
sqrt(6 * 1.0 + 3 * 4.0 + 12 * 16.0 + 4 * 25.0 + 20 * 81.0 + 5 * 100.0),
kTolerance);
}
TEST_F(BlockRandomAccessDiagonalMatrixTest, RightMultiplyAndAccumulate) {
double kTolerance = 1e-14;
auto* crsm = m_->matrix();
Matrix dense;
crsm->ToDenseMatrix(&dense);
Vector x = Vector::Random(dense.rows());
Vector expected_y = dense * x;
Vector actual_y = Vector::Zero(dense.rows());
m_->RightMultiplyAndAccumulate(x.data(), actual_y.data());
EXPECT_NEAR((expected_y - actual_y).norm(), 0, kTolerance);
}
TEST_F(BlockRandomAccessDiagonalMatrixTest, Invert) {
double kTolerance = 1e-14;
auto* crsm = m_->matrix();
Matrix dense;
crsm->ToDenseMatrix(&dense);
Matrix expected_inverse =
dense.llt().solve(Matrix::Identity(dense.rows(), dense.rows()));
m_->Invert();
crsm->ToDenseMatrix(&dense);
EXPECT_NEAR((expected_inverse - dense).norm(), 0.0, kTolerance);
}
} // namespace ceres::internal