blob: ab6914863a6a337f9189cfad9d28c755e9ca4157 [file] [log] [blame]
// 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:
//
// * 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
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_jacobi_preconditioner.h"
#include <memory>
#include <vector>
#include "Eigen/Dense"
#include "ceres/block_random_access_diagonal_matrix.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/linear_least_squares_problems.h"
#include "gtest/gtest.h"
namespace ceres::internal {
class BlockJacobiPreconditionerTest : public ::testing::Test {
protected:
void SetUpFromProblemId(int problem_id) {
std::unique_ptr<LinearLeastSquaresProblem> problem =
CreateLinearLeastSquaresProblemFromId(problem_id);
CHECK(problem != nullptr);
A.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
D = std::move(problem->D);
Matrix dense_a;
A->ToDenseMatrix(&dense_a);
dense_ata = dense_a.transpose() * dense_a;
dense_ata += VectorRef(D.get(), A->num_cols())
.array()
.square()
.matrix()
.asDiagonal();
}
void VerifyDiagonalBlocks(const int problem_id) {
SetUpFromProblemId(problem_id);
BlockJacobiPreconditioner pre(*A);
pre.Update(*A, D.get());
auto* m = const_cast<BlockRandomAccessDiagonalMatrix*>(&pre.matrix());
EXPECT_EQ(m->num_rows(), A->num_cols());
EXPECT_EQ(m->num_cols(), A->num_cols());
const CompressedRowBlockStructure* bs = A->block_structure();
for (int i = 0; i < bs->cols.size(); ++i) {
const int block_size = bs->cols[i].size;
int r, c, row_stride, col_stride;
CellInfo* cell_info = m->GetCell(i, i, &r, &c, &row_stride, &col_stride);
MatrixRef m(cell_info->values, row_stride, col_stride);
Matrix actual_block_inverse = m.block(r, c, block_size, block_size);
Matrix expected_block = dense_ata.block(
bs->cols[i].position, bs->cols[i].position, block_size, block_size);
const double residual = (actual_block_inverse * expected_block -
Matrix::Identity(block_size, block_size))
.norm();
EXPECT_NEAR(residual, 0.0, 1e-12) << "Block: " << i;
}
}
std::unique_ptr<BlockSparseMatrix> A;
std::unique_ptr<double[]> D;
Matrix dense_ata;
};
TEST_F(BlockJacobiPreconditionerTest, SmallProblem) { VerifyDiagonalBlocks(2); }
TEST_F(BlockJacobiPreconditionerTest, LargeProblem) { VerifyDiagonalBlocks(3); }
} // namespace ceres::internal