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https://github.com/ceres-solver/ceres-solver.git
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39ec5e8f99
With this change, the user can now choose between Approximate Minimum Degree and Nested Dissection as a fill reducing algorithm when using a sparse direct factorization based linear solver like SPARSE_NORMAL_CHOLESKY or SPARSE_SCHUR. Currenly only SUITE_SPARSE is supported. It requires that SuiteSparse be compiled with Metis support enabled. On most problems AMD is still the better choice, but in some cases like the grid3D dataset from https://lucacarlone.mit.edu/datasets/ the solution time with AMD is 57s and with NESDIS 38 on my M1 Mac. On some other problems at Google we have observed speedups of 10x, there is also a corresponding decrease in the total amount of memory used. This patch is based on the original work done by NeroBurner in https://ceres-solver-review.googlesource.com/c/ceres-solver/+/20580 1. Add a new enum to the public api LinearSolverOrderingType and a setting Solver::Options::linear_solver_ordering_type. 2. TrustRegionPreprocessor had some complicated logic which determined when linear solvers should reorder their matrices on their own and not this has been refactored into a more readable function that lives inside reorder_program.h/cc. 3. Plumbing in reorder_program.cc and trust_region_processor.cc to use nested dissection. 4. Update bundle_adjuster.cc to use nested dissection. Change-Id: I388b027934f86c58b4da2b65a4fa5204ea73bf40
289 lines
11 KiB
C++
289 lines
11 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 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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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include "ceres/reorder_program.h"
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#include <random>
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#include "ceres/internal/config.h"
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#include "ceres/parameter_block.h"
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#include "ceres/problem_impl.h"
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#include "ceres/program.h"
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#include "ceres/sized_cost_function.h"
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#include "ceres/solver.h"
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#include "gmock/gmock.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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using std::vector;
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// Templated base class for the CostFunction signatures.
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template <int kNumResiduals, int... Ns>
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class MockCostFunctionBase : public SizedCostFunction<kNumResiduals, Ns...> {
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public:
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bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const final {
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// Do nothing. This is never called.
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return true;
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}
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};
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class UnaryCostFunction : public MockCostFunctionBase<2, 1> {};
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class BinaryCostFunction : public MockCostFunctionBase<2, 1, 1> {};
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class TernaryCostFunction : public MockCostFunctionBase<2, 1, 1, 1> {};
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TEST(_, ReorderResidualBlockNormalFunction) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &x);
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problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &z, &x);
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problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &z, &y);
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problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &z);
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problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &x, &y);
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problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &y);
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auto linear_solver_ordering = std::make_shared<ParameterBlockOrdering>();
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linear_solver_ordering->AddElementToGroup(&x, 0);
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linear_solver_ordering->AddElementToGroup(&y, 0);
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linear_solver_ordering->AddElementToGroup(&z, 1);
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Solver::Options options;
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options.linear_solver_type = DENSE_SCHUR;
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options.linear_solver_ordering = linear_solver_ordering;
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const vector<ResidualBlock*>& residual_blocks =
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problem.program().residual_blocks();
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vector<ResidualBlock*> expected_residual_blocks;
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// This is a bit fragile, but it serves the purpose. We know the
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// bucketing algorithm that the reordering function uses, so we
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// expect the order for residual blocks for each e_block to be
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// filled in reverse.
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expected_residual_blocks.push_back(residual_blocks[4]);
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expected_residual_blocks.push_back(residual_blocks[1]);
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expected_residual_blocks.push_back(residual_blocks[0]);
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expected_residual_blocks.push_back(residual_blocks[5]);
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expected_residual_blocks.push_back(residual_blocks[2]);
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expected_residual_blocks.push_back(residual_blocks[3]);
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Program* program = problem.mutable_program();
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program->SetParameterOffsetsAndIndex();
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std::string message;
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EXPECT_TRUE(LexicographicallyOrderResidualBlocks(
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2, problem.mutable_program(), &message));
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EXPECT_EQ(residual_blocks.size(), expected_residual_blocks.size());
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for (int i = 0; i < expected_residual_blocks.size(); ++i) {
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EXPECT_EQ(residual_blocks[i], expected_residual_blocks[i]);
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}
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}
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TEST(_, ApplyOrderingOrderingTooSmall) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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ParameterBlockOrdering linear_solver_ordering;
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linear_solver_ordering.AddElementToGroup(&x, 0);
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linear_solver_ordering.AddElementToGroup(&y, 1);
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Program program(problem.program());
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std::string message;
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EXPECT_FALSE(ApplyOrdering(
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problem.parameter_map(), linear_solver_ordering, &program, &message));
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}
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TEST(_, ApplyOrderingNormal) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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ParameterBlockOrdering linear_solver_ordering;
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linear_solver_ordering.AddElementToGroup(&x, 0);
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linear_solver_ordering.AddElementToGroup(&y, 2);
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linear_solver_ordering.AddElementToGroup(&z, 1);
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Program* program = problem.mutable_program();
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std::string message;
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EXPECT_TRUE(ApplyOrdering(
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problem.parameter_map(), linear_solver_ordering, program, &message));
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const vector<ParameterBlock*>& parameter_blocks = program->parameter_blocks();
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EXPECT_EQ(parameter_blocks.size(), 3);
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EXPECT_EQ(parameter_blocks[0]->user_state(), &x);
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EXPECT_EQ(parameter_blocks[1]->user_state(), &z);
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EXPECT_EQ(parameter_blocks[2]->user_state(), &y);
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}
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#ifndef CERES_NO_SUITESPARSE
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class ReorderProgramForSparseCholeskyUsingSuiteSparseTest
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: public ::testing::Test {
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protected:
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void SetUp() override {
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problem_.AddResidualBlock(new UnaryCostFunction(), nullptr, &x_);
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problem_.AddResidualBlock(new BinaryCostFunction(), nullptr, &z_, &x_);
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problem_.AddResidualBlock(new BinaryCostFunction(), nullptr, &z_, &y_);
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problem_.AddResidualBlock(new UnaryCostFunction(), nullptr, &z_);
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problem_.AddResidualBlock(new BinaryCostFunction(), nullptr, &x_, &y_);
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problem_.AddResidualBlock(new UnaryCostFunction(), nullptr, &y_);
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}
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void ComputeAndValidateOrdering(
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const ParameterBlockOrdering& linear_solver_ordering) {
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Program* program = problem_.mutable_program();
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vector<ParameterBlock*> unordered_parameter_blocks =
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program->parameter_blocks();
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std::string error;
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EXPECT_TRUE(ReorderProgramForSparseCholesky(ceres::SUITE_SPARSE,
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ceres::AMD,
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linear_solver_ordering,
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0, /* use all rows */
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program,
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&error));
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const vector<ParameterBlock*>& ordered_parameter_blocks =
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program->parameter_blocks();
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EXPECT_EQ(ordered_parameter_blocks.size(),
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unordered_parameter_blocks.size());
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EXPECT_THAT(unordered_parameter_blocks,
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::testing::UnorderedElementsAreArray(ordered_parameter_blocks));
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}
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ProblemImpl problem_;
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double x_;
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double y_;
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double z_;
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};
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TEST_F(ReorderProgramForSparseCholeskyUsingSuiteSparseTest,
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EverythingInGroupZero) {
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ParameterBlockOrdering linear_solver_ordering;
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linear_solver_ordering.AddElementToGroup(&x_, 0);
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linear_solver_ordering.AddElementToGroup(&y_, 0);
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linear_solver_ordering.AddElementToGroup(&z_, 0);
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ComputeAndValidateOrdering(linear_solver_ordering);
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}
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TEST_F(ReorderProgramForSparseCholeskyUsingSuiteSparseTest, ContiguousGroups) {
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ParameterBlockOrdering linear_solver_ordering;
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linear_solver_ordering.AddElementToGroup(&x_, 0);
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linear_solver_ordering.AddElementToGroup(&y_, 1);
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linear_solver_ordering.AddElementToGroup(&z_, 2);
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ComputeAndValidateOrdering(linear_solver_ordering);
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}
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TEST_F(ReorderProgramForSparseCholeskyUsingSuiteSparseTest, GroupsWithGaps) {
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ParameterBlockOrdering linear_solver_ordering;
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linear_solver_ordering.AddElementToGroup(&x_, 0);
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linear_solver_ordering.AddElementToGroup(&y_, 2);
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linear_solver_ordering.AddElementToGroup(&z_, 2);
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ComputeAndValidateOrdering(linear_solver_ordering);
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}
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TEST_F(ReorderProgramForSparseCholeskyUsingSuiteSparseTest,
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NonContiguousStartingAtTwo) {
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ParameterBlockOrdering linear_solver_ordering;
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linear_solver_ordering.AddElementToGroup(&x_, 2);
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linear_solver_ordering.AddElementToGroup(&y_, 4);
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linear_solver_ordering.AddElementToGroup(&z_, 4);
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ComputeAndValidateOrdering(linear_solver_ordering);
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}
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#endif // CERES_NO_SUITESPARSE
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TEST(_, ReorderResidualBlocksbyPartition) {
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ProblemImpl problem;
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double x;
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double y;
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double z;
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problem.AddParameterBlock(&x, 1);
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problem.AddParameterBlock(&y, 1);
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problem.AddParameterBlock(&z, 1);
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problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &x);
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problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &z, &x);
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problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &z, &y);
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problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &z);
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problem.AddResidualBlock(new BinaryCostFunction(), nullptr, &x, &y);
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problem.AddResidualBlock(new UnaryCostFunction(), nullptr, &y);
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std::vector<ResidualBlockId> residual_block_ids;
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problem.GetResidualBlocks(&residual_block_ids);
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std::vector<ResidualBlock*> residual_blocks =
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problem.program().residual_blocks();
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auto rng = std::default_random_engine{};
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for (int i = 1; i < 6; ++i) {
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std::shuffle(
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std::begin(residual_block_ids), std::end(residual_block_ids), rng);
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std::unordered_set<ResidualBlockId> bottom(residual_block_ids.begin(),
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residual_block_ids.begin() + i);
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const int start_bottom =
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ReorderResidualBlocksByPartition(bottom, problem.mutable_program());
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std::vector<ResidualBlock*> actual_residual_blocks =
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problem.program().residual_blocks();
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EXPECT_THAT(actual_residual_blocks,
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testing::UnorderedElementsAreArray(residual_blocks));
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EXPECT_EQ(start_bottom, residual_blocks.size() - i);
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for (int j = start_bottom; j < residual_blocks.size(); ++j) {
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EXPECT_THAT(bottom, ::testing::Contains(actual_residual_blocks[j]));
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}
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}
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}
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} // namespace internal
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} // namespace ceres
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