252 lines
8.6 KiB
C++
252 lines
8.6 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/casts.h"
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#include "ceres/compressed_row_sparse_matrix.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/linear_least_squares_problems.h"
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#include "ceres/linear_solver.h"
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#include "ceres/triplet_sparse_matrix.h"
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#include "ceres/types.h"
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#include "glog/logging.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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class UnsymmetricLinearSolverTest : public ::testing::Test {
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protected :
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virtual void SetUp() {
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scoped_ptr<LinearLeastSquaresProblem> problem(
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CreateLinearLeastSquaresProblemFromId(0));
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CHECK_NOTNULL(problem.get());
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A_.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
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b_.reset(problem->b.release());
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D_.reset(problem->D.release());
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sol_unregularized_.reset(problem->x.release());
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sol_regularized_.reset(problem->x_D.release());
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}
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void TestSolver(const LinearSolver::Options& options) {
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LinearSolver::PerSolveOptions per_solve_options;
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LinearSolver::Summary unregularized_solve_summary;
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LinearSolver::Summary regularized_solve_summary;
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Vector x_unregularized(A_->num_cols());
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Vector x_regularized(A_->num_cols());
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scoped_ptr<SparseMatrix> transformed_A;
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if (options.type == DENSE_QR ||
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options.type == DENSE_NORMAL_CHOLESKY) {
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transformed_A.reset(new DenseSparseMatrix(*A_));
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} else if (options.type == SPARSE_NORMAL_CHOLESKY) {
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CompressedRowSparseMatrix* crsm = new CompressedRowSparseMatrix(*A_);
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// Add row/column blocks structure.
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for (int i = 0; i < A_->num_rows(); ++i) {
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crsm->mutable_row_blocks()->push_back(1);
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}
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for (int i = 0; i < A_->num_cols(); ++i) {
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crsm->mutable_col_blocks()->push_back(1);
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}
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transformed_A.reset(crsm);
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} else {
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LOG(FATAL) << "Unknown linear solver : " << options.type;
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}
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// Unregularized
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scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
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unregularized_solve_summary =
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solver->Solve(transformed_A.get(),
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b_.get(),
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per_solve_options,
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x_unregularized.data());
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// Sparsity structure is changing, reset the solver.
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solver.reset(LinearSolver::Create(options));
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// Regularized solution
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per_solve_options.D = D_.get();
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regularized_solve_summary =
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solver->Solve(transformed_A.get(),
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b_.get(),
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per_solve_options,
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x_regularized.data());
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EXPECT_EQ(unregularized_solve_summary.termination_type,
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LINEAR_SOLVER_SUCCESS);
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for (int i = 0; i < A_->num_cols(); ++i) {
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EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8)
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<< "\nExpected: "
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<< ConstVectorRef(sol_unregularized_.get(),
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A_->num_cols()).transpose()
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<< "\nActual: " << x_unregularized.transpose();
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}
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EXPECT_EQ(regularized_solve_summary.termination_type,
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LINEAR_SOLVER_SUCCESS);
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for (int i = 0; i < A_->num_cols(); ++i) {
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EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8)
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<< "\nExpected: "
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<< ConstVectorRef(sol_regularized_.get(), A_->num_cols()).transpose()
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<< "\nActual: " << x_regularized.transpose();
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}
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}
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scoped_ptr<TripletSparseMatrix> A_;
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scoped_array<double> b_;
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scoped_array<double> D_;
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scoped_array<double> sol_unregularized_;
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scoped_array<double> sol_regularized_;
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};
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TEST_F(UnsymmetricLinearSolverTest, EigenDenseQR) {
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LinearSolver::Options options;
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options.type = DENSE_QR;
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options.dense_linear_algebra_library_type = EIGEN;
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TestSolver(options);
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}
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TEST_F(UnsymmetricLinearSolverTest, EigenDenseNormalCholesky) {
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LinearSolver::Options options;
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options.dense_linear_algebra_library_type = EIGEN;
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options.type = DENSE_NORMAL_CHOLESKY;
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TestSolver(options);
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}
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#ifndef CERES_NO_LAPACK
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TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseQR) {
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LinearSolver::Options options;
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options.type = DENSE_QR;
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options.dense_linear_algebra_library_type = LAPACK;
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TestSolver(options);
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}
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TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseNormalCholesky) {
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LinearSolver::Options options;
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options.dense_linear_algebra_library_type = LAPACK;
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options.type = DENSE_NORMAL_CHOLESKY;
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TestSolver(options);
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}
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#endif
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#ifndef CERES_NO_SUITESPARSE
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = SUITE_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.use_postordering = false;
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TestSolver(options);
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}
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = SUITE_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.use_postordering = true;
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TestSolver(options);
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}
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingSuiteSparseDynamicSparsity) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = SUITE_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.dynamic_sparsity = true;
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TestSolver(options);
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}
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#endif
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#ifndef CERES_NO_CXSPARSE
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingCXSparsePreOrdering) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = CX_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.use_postordering = false;
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TestSolver(options);
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}
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingCXSparsePostOrdering) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = CX_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.use_postordering = true;
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TestSolver(options);
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}
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingCXSparseDynamicSparsity) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = CX_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.dynamic_sparsity = true;
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TestSolver(options);
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}
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#endif
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#ifdef CERES_USE_EIGEN_SPARSE
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingEigenPreOrdering) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.use_postordering = false;
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TestSolver(options);
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}
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingEigenPostOrdering) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.use_postordering = true;
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TestSolver(options);
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}
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TEST_F(UnsymmetricLinearSolverTest,
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SparseNormalCholeskyUsingEigenDynamicSparsity) {
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LinearSolver::Options options;
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options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
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options.type = SPARSE_NORMAL_CHOLESKY;
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options.dynamic_sparsity = true;
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TestSolver(options);
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}
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#endif // CERES_USE_EIGEN_SPARSE
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} // namespace internal
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} // namespace ceres
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