mirror of
https://github.com/ceres-solver/ceres-solver.git
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8cb441c49a
- Follow up to commit https://github.com/ceres-solver/ceres-solver/commit/54ba6c27b504f43b59b0d91da0165995c66d9a3d which enabled -Wmissing-declarations and fixed all previous infractions. Change-Id: Ia10b49b4c9bc12c0f7bec2b8c0706f38be18f1dc
223 lines
8.2 KiB
C++
223 lines
8.2 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2019 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Authors: sameeragarwal@google.com (Sameer Agarwal)
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#include "Eigen/Dense"
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#include "benchmark/benchmark.h"
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#include "ceres/block_random_access_dense_matrix.h"
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#include "ceres/block_sparse_matrix.h"
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#include "ceres/block_structure.h"
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#include "ceres/random.h"
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#include "ceres/schur_eliminator.h"
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namespace ceres {
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namespace internal {
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constexpr int kRowBlockSize = 2;
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constexpr int kEBlockSize = 3;
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constexpr int kFBlockSize = 6;
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class BenchmarkData {
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public:
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explicit BenchmarkData(const int num_e_blocks) {
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CompressedRowBlockStructure* bs = new CompressedRowBlockStructure;
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bs->cols.resize(num_e_blocks + 1);
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int col_pos = 0;
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for (int i = 0; i < num_e_blocks; ++i) {
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bs->cols[i].position = col_pos;
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bs->cols[i].size = kEBlockSize;
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col_pos += kEBlockSize;
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}
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bs->cols.back().position = col_pos;
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bs->cols.back().size = kFBlockSize;
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bs->rows.resize(2 * num_e_blocks);
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int row_pos = 0;
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int cell_pos = 0;
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for (int i = 0; i < num_e_blocks; ++i) {
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{
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auto& row = bs->rows[2 * i];
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row.block.position = row_pos;
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row.block.size = kRowBlockSize;
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row_pos += kRowBlockSize;
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auto& cells = row.cells;
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cells.resize(2);
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cells[0].block_id = i;
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cells[0].position = cell_pos;
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cell_pos += kRowBlockSize * kEBlockSize;
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cells[1].block_id = num_e_blocks;
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cells[1].position = cell_pos;
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cell_pos += kRowBlockSize * kFBlockSize;
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}
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{
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auto& row = bs->rows[2 * i + 1];
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row.block.position = row_pos;
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row.block.size = kRowBlockSize;
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row_pos += kRowBlockSize;
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auto& cells = row.cells;
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cells.resize(1);
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cells[0].block_id = i;
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cells[0].position = cell_pos;
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cell_pos += kRowBlockSize * kEBlockSize;
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}
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}
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matrix_.reset(new BlockSparseMatrix(bs));
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double* values = matrix_->mutable_values();
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for (int i = 0; i < matrix_->num_nonzeros(); ++i) {
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values[i] = RandNormal();
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}
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b_.resize(matrix_->num_rows());
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b_.setRandom();
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std::vector<int> blocks(1, kFBlockSize);
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lhs_.reset(new BlockRandomAccessDenseMatrix(blocks));
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diagonal_.resize(matrix_->num_cols());
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diagonal_.setOnes();
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rhs_.resize(kFBlockSize);
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y_.resize(num_e_blocks * kEBlockSize);
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y_.setZero();
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z_.resize(kFBlockSize);
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z_.setOnes();
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}
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const BlockSparseMatrix& matrix() const { return *matrix_; }
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const Vector& b() const { return b_; }
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const Vector& diagonal() const { return diagonal_; }
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BlockRandomAccessDenseMatrix* mutable_lhs() { return lhs_.get(); }
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Vector* mutable_rhs() { return &rhs_; }
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Vector* mutable_y() { return &y_; }
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Vector* mutable_z() { return &z_; }
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private:
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std::unique_ptr<BlockSparseMatrix> matrix_;
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Vector b_;
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std::unique_ptr<BlockRandomAccessDenseMatrix> lhs_;
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Vector rhs_;
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Vector diagonal_;
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Vector z_;
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Vector y_;
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};
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static void BM_SchurEliminatorEliminate(benchmark::State& state) {
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const int num_e_blocks = state.range(0);
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BenchmarkData data(num_e_blocks);
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ContextImpl context;
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LinearSolver::Options linear_solver_options;
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linear_solver_options.e_block_size = kEBlockSize;
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linear_solver_options.row_block_size = kRowBlockSize;
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linear_solver_options.f_block_size = kFBlockSize;
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linear_solver_options.context = &context;
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std::unique_ptr<SchurEliminatorBase> eliminator(
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SchurEliminatorBase::Create(linear_solver_options));
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eliminator->Init(num_e_blocks, true, data.matrix().block_structure());
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for (auto _ : state) {
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eliminator->Eliminate(BlockSparseMatrixData(data.matrix()),
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data.b().data(),
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data.diagonal().data(),
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data.mutable_lhs(),
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data.mutable_rhs()->data());
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}
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}
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static void BM_SchurEliminatorBackSubstitute(benchmark::State& state) {
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const int num_e_blocks = state.range(0);
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BenchmarkData data(num_e_blocks);
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ContextImpl context;
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LinearSolver::Options linear_solver_options;
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linear_solver_options.e_block_size = kEBlockSize;
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linear_solver_options.row_block_size = kRowBlockSize;
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linear_solver_options.f_block_size = kFBlockSize;
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linear_solver_options.context = &context;
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std::unique_ptr<SchurEliminatorBase> eliminator(
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SchurEliminatorBase::Create(linear_solver_options));
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eliminator->Init(num_e_blocks, true, data.matrix().block_structure());
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eliminator->Eliminate(BlockSparseMatrixData(data.matrix()),
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data.b().data(),
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data.diagonal().data(),
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data.mutable_lhs(),
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data.mutable_rhs()->data());
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for (auto _ : state) {
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eliminator->BackSubstitute(BlockSparseMatrixData(data.matrix()),
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data.b().data(),
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data.diagonal().data(),
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data.mutable_z()->data(),
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data.mutable_y()->data());
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}
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}
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static void BM_SchurEliminatorForOneFBlockEliminate(benchmark::State& state) {
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const int num_e_blocks = state.range(0);
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BenchmarkData data(num_e_blocks);
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SchurEliminatorForOneFBlock<2, 3, 6> eliminator;
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eliminator.Init(num_e_blocks, true, data.matrix().block_structure());
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for (auto _ : state) {
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eliminator.Eliminate(BlockSparseMatrixData(data.matrix()),
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data.b().data(),
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data.diagonal().data(),
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data.mutable_lhs(),
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data.mutable_rhs()->data());
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}
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}
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static void BM_SchurEliminatorForOneFBlockBackSubstitute(benchmark::State& state) {
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const int num_e_blocks = state.range(0);
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BenchmarkData data(num_e_blocks);
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SchurEliminatorForOneFBlock<2, 3, 6> eliminator;
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eliminator.Init(num_e_blocks, true, data.matrix().block_structure());
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eliminator.Eliminate(BlockSparseMatrixData(data.matrix()),
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data.b().data(),
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data.diagonal().data(),
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data.mutable_lhs(),
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data.mutable_rhs()->data());
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for (auto _ : state) {
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eliminator.BackSubstitute(BlockSparseMatrixData(data.matrix()),
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data.b().data(),
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data.diagonal().data(),
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data.mutable_z()->data(),
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data.mutable_y()->data());
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}
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}
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BENCHMARK(BM_SchurEliminatorEliminate)->Range(10, 10000);
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BENCHMARK(BM_SchurEliminatorForOneFBlockEliminate)->Range(10, 10000);
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BENCHMARK(BM_SchurEliminatorBackSubstitute)->Range(10, 10000);
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BENCHMARK(BM_SchurEliminatorForOneFBlockBackSubstitute)->Range(10, 10000);
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} // namespace internal
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} // namespace ceres
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BENCHMARK_MAIN();
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