internal/ceres/unsymmetric_linear_solver_test.cc - platform/external/ceres-solver - Git at Google

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
 // http://code.google.com/p/ceres-solver/
 //
 // 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/casts.h"
 #include "ceres/compressed_row_sparse_matrix.h"
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/linear_least_squares_problems.h"
 #include "ceres/linear_solver.h"
 #include "ceres/triplet_sparse_matrix.h"
 #include "ceres/types.h"
 #include "glog/logging.h"
 #include "gtest/gtest.h"


 namespace ceres {
 namespace internal {

 class UnsymmetricLinearSolverTest : public ::testing::Test {
  protected :
   virtual void SetUp() {
     scoped_ptr<LinearLeastSquaresProblem> problem(
         CreateLinearLeastSquaresProblemFromId(0));

     CHECK_NOTNULL(problem.get());
     A_.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
     b_.reset(problem->b.release());
     D_.reset(problem->D.release());
     sol_unregularized_.reset(problem->x.release());
     sol_regularized_.reset(problem->x_D.release());
   }

   void TestSolver(const LinearSolver::Options& options) {


     LinearSolver::PerSolveOptions per_solve_options;
     LinearSolver::Summary unregularized_solve_summary;
     LinearSolver::Summary regularized_solve_summary;
     Vector x_unregularized(A_->num_cols());
     Vector x_regularized(A_->num_cols());

     scoped_ptr<SparseMatrix> transformed_A;

     if (options.type == DENSE_QR ||
         options.type == DENSE_NORMAL_CHOLESKY) {
       transformed_A.reset(new DenseSparseMatrix(*A_));
     } else if (options.type == SPARSE_NORMAL_CHOLESKY) {
       CompressedRowSparseMatrix* crsm =  new CompressedRowSparseMatrix(*A_);
       // Add row/column blocks structure.
       for (int i = 0; i < A_->num_rows(); ++i) {
         crsm->mutable_row_blocks()->push_back(1);
       }

       for (int i = 0; i < A_->num_cols(); ++i) {
         crsm->mutable_col_blocks()->push_back(1);
       }
       transformed_A.reset(crsm);
     } else {
       LOG(FATAL) << "Unknown linear solver : " << options.type;
     }

     // Unregularized
     scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
     unregularized_solve_summary =
         solver->Solve(transformed_A.get(),
                       b_.get(),
                       per_solve_options,
                       x_unregularized.data());

     // Sparsity structure is changing, reset the solver.
     solver.reset(LinearSolver::Create(options));
     // Regularized solution
     per_solve_options.D = D_.get();
     regularized_solve_summary =
         solver->Solve(transformed_A.get(),
                       b_.get(),
                       per_solve_options,
                       x_regularized.data());

     EXPECT_EQ(unregularized_solve_summary.termination_type,
               LINEAR_SOLVER_SUCCESS);

     for (int i = 0; i < A_->num_cols(); ++i) {
       EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8)
           << "\nExpected: "
           << ConstVectorRef(sol_unregularized_.get(), A_->num_cols()).transpose()
           << "\nActual: " << x_unregularized.transpose();
     }

     EXPECT_EQ(regularized_solve_summary.termination_type,
               LINEAR_SOLVER_SUCCESS);
     for (int i = 0; i < A_->num_cols(); ++i) {
       EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8)
           << "\nExpected: "
           << ConstVectorRef(sol_regularized_.get(), A_->num_cols()).transpose()
           << "\nActual: " << x_regularized.transpose();
     }
   }

   scoped_ptr<TripletSparseMatrix> A_;
   scoped_array<double> b_;
   scoped_array<double> D_;
   scoped_array<double> sol_unregularized_;
   scoped_array<double> sol_regularized_;
 };

 TEST_F(UnsymmetricLinearSolverTest, EigenDenseQR) {
   LinearSolver::Options options;
   options.type = DENSE_QR;
   options.dense_linear_algebra_library_type = EIGEN;
   TestSolver(options);
 }

 TEST_F(UnsymmetricLinearSolverTest, EigenDenseNormalCholesky) {
   LinearSolver::Options options;
   options.dense_linear_algebra_library_type = EIGEN;
   options.type = DENSE_NORMAL_CHOLESKY;
   TestSolver(options);
 }

 #ifndef CERES_NO_LAPACK
 TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseQR) {
   LinearSolver::Options options;
   options.type = DENSE_QR;
   options.dense_linear_algebra_library_type = LAPACK;
   TestSolver(options);
 }

 TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseNormalCholesky) {
   LinearSolver::Options options;
   options.dense_linear_algebra_library_type = LAPACK;
   options.type = DENSE_NORMAL_CHOLESKY;
   TestSolver(options);
 }
 #endif

 #ifndef CERES_NO_SUITESPARSE
 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = SUITE_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.use_postordering = false;
   TestSolver(options);
 }

 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = SUITE_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.use_postordering = true;
   TestSolver(options);
 }

 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingSuiteSparseDynamicSparsity) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = SUITE_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.dynamic_sparsity = true;
   TestSolver(options);
 }
 #endif

 #ifndef CERES_NO_CXSPARSE
 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingCXSparsePreOrdering) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = CX_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.use_postordering = false;
   TestSolver(options);
 }

 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingCXSparsePostOrdering) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = CX_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.use_postordering = true;
   TestSolver(options);
 }

 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingCXSparseDynamicSparsity) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = CX_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.dynamic_sparsity = true;
   TestSolver(options);
 }
 #endif

 #ifdef CERES_USE_EIGEN_SPARSE
 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingEigenPreOrdering) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.use_postordering = false;
   TestSolver(options);
 }

 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingEigenPostOrdering) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.use_postordering = true;
   TestSolver(options);
 }

 TEST_F(UnsymmetricLinearSolverTest,
        SparseNormalCholeskyUsingEigenDynamicSparsity) {
   LinearSolver::Options options;
   options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
   options.type = SPARSE_NORMAL_CHOLESKY;
   options.dynamic_sparsity = true;
   TestSolver(options);
 }

 #endif  // CERES_USE_EIGEN_SPARSE

 }  // namespace internal
 }  // namespace ceres
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
	// http://code.google.com/p/ceres-solver/
	//
	// 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/casts.h"
	#include "ceres/compressed_row_sparse_matrix.h"
	#include "ceres/internal/scoped_ptr.h"
	#include "ceres/linear_least_squares_problems.h"
	#include "ceres/linear_solver.h"
	#include "ceres/triplet_sparse_matrix.h"
	#include "ceres/types.h"
	#include "glog/logging.h"
	#include "gtest/gtest.h"


	namespace ceres {
	namespace internal {

	class UnsymmetricLinearSolverTest : public ::testing::Test {
	protected :
	virtual void SetUp() {
	scoped_ptr<LinearLeastSquaresProblem> problem(
	CreateLinearLeastSquaresProblemFromId(0));

	CHECK_NOTNULL(problem.get());
	A_.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
	b_.reset(problem->b.release());
	D_.reset(problem->D.release());
	sol_unregularized_.reset(problem->x.release());
	sol_regularized_.reset(problem->x_D.release());
	}

	void TestSolver(const LinearSolver::Options& options) {


	LinearSolver::PerSolveOptions per_solve_options;
	LinearSolver::Summary unregularized_solve_summary;
	LinearSolver::Summary regularized_solve_summary;
	Vector x_unregularized(A_->num_cols());
	Vector x_regularized(A_->num_cols());

	scoped_ptr<SparseMatrix> transformed_A;

	if (options.type == DENSE_QR \|\|
	options.type == DENSE_NORMAL_CHOLESKY) {
	transformed_A.reset(new DenseSparseMatrix(*A_));
	} else if (options.type == SPARSE_NORMAL_CHOLESKY) {
	CompressedRowSparseMatrix* crsm = new CompressedRowSparseMatrix(*A_);
	// Add row/column blocks structure.
	for (int i = 0; i < A_->num_rows(); ++i) {
	crsm->mutable_row_blocks()->push_back(1);
	}

	for (int i = 0; i < A_->num_cols(); ++i) {
	crsm->mutable_col_blocks()->push_back(1);
	}
	transformed_A.reset(crsm);
	} else {
	LOG(FATAL) << "Unknown linear solver : " << options.type;
	}

	// Unregularized
	scoped_ptr<LinearSolver> solver(LinearSolver::Create(options));
	unregularized_solve_summary =
	solver->Solve(transformed_A.get(),
	b_.get(),
	per_solve_options,
	x_unregularized.data());

	// Sparsity structure is changing, reset the solver.
	solver.reset(LinearSolver::Create(options));
	// Regularized solution
	per_solve_options.D = D_.get();
	regularized_solve_summary =
	solver->Solve(transformed_A.get(),
	b_.get(),
	per_solve_options,
	x_regularized.data());

	EXPECT_EQ(unregularized_solve_summary.termination_type,
	LINEAR_SOLVER_SUCCESS);

	for (int i = 0; i < A_->num_cols(); ++i) {
	EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8)
	<< "\nExpected: "
	<< ConstVectorRef(sol_unregularized_.get(), A_->num_cols()).transpose()
	<< "\nActual: " << x_unregularized.transpose();
	}

	EXPECT_EQ(regularized_solve_summary.termination_type,
	LINEAR_SOLVER_SUCCESS);
	for (int i = 0; i < A_->num_cols(); ++i) {
	EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8)
	<< "\nExpected: "
	<< ConstVectorRef(sol_regularized_.get(), A_->num_cols()).transpose()
	<< "\nActual: " << x_regularized.transpose();
	}
	}

	scoped_ptr<TripletSparseMatrix> A_;
	scoped_array<double> b_;
	scoped_array<double> D_;
	scoped_array<double> sol_unregularized_;
	scoped_array<double> sol_regularized_;
	};

	TEST_F(UnsymmetricLinearSolverTest, EigenDenseQR) {
	LinearSolver::Options options;
	options.type = DENSE_QR;
	options.dense_linear_algebra_library_type = EIGEN;
	TestSolver(options);
	}

	TEST_F(UnsymmetricLinearSolverTest, EigenDenseNormalCholesky) {
	LinearSolver::Options options;
	options.dense_linear_algebra_library_type = EIGEN;
	options.type = DENSE_NORMAL_CHOLESKY;
	TestSolver(options);
	}

	#ifndef CERES_NO_LAPACK
	TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseQR) {
	LinearSolver::Options options;
	options.type = DENSE_QR;
	options.dense_linear_algebra_library_type = LAPACK;
	TestSolver(options);
	}

	TEST_F(UnsymmetricLinearSolverTest, LAPACKDenseNormalCholesky) {
	LinearSolver::Options options;
	options.dense_linear_algebra_library_type = LAPACK;
	options.type = DENSE_NORMAL_CHOLESKY;
	TestSolver(options);
	}
	#endif

	#ifndef CERES_NO_SUITESPARSE
	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = SUITE_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.use_postordering = false;
	TestSolver(options);
	}

	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = SUITE_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.use_postordering = true;
	TestSolver(options);
	}

	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingSuiteSparseDynamicSparsity) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = SUITE_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.dynamic_sparsity = true;
	TestSolver(options);
	}
	#endif

	#ifndef CERES_NO_CXSPARSE
	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingCXSparsePreOrdering) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = CX_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.use_postordering = false;
	TestSolver(options);
	}

	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingCXSparsePostOrdering) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = CX_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.use_postordering = true;
	TestSolver(options);
	}

	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingCXSparseDynamicSparsity) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = CX_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.dynamic_sparsity = true;
	TestSolver(options);
	}
	#endif

	#ifdef CERES_USE_EIGEN_SPARSE
	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingEigenPreOrdering) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.use_postordering = false;
	TestSolver(options);
	}

	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingEigenPostOrdering) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.use_postordering = true;
	TestSolver(options);
	}

	TEST_F(UnsymmetricLinearSolverTest,
	SparseNormalCholeskyUsingEigenDynamicSparsity) {
	LinearSolver::Options options;
	options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
	options.type = SPARSE_NORMAL_CHOLESKY;
	options.dynamic_sparsity = true;
	TestSolver(options);
	}

	#endif // CERES_USE_EIGEN_SPARSE

	} // namespace internal
	} // namespace ceres