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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 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/sparse_cholesky.h"
#include <memory>
#include "ceres/accelerate_sparse.h"
#include "ceres/cxsparse.h"
#include "ceres/eigensparse.h"
#include "ceres/float_cxsparse.h"
#include "ceres/float_suitesparse.h"
#include "ceres/iterative_refiner.h"
#include "ceres/suitesparse.h"
namespace ceres {
namespace internal {
std::unique_ptr<SparseCholesky> SparseCholesky::Create(
const LinearSolver::Options& options) {
const OrderingType ordering_type = options.use_postordering ? AMD : NATURAL;
std::unique_ptr<SparseCholesky> sparse_cholesky;
switch (options.sparse_linear_algebra_library_type) {
case SUITE_SPARSE:
#ifndef CERES_NO_SUITESPARSE
if (options.use_mixed_precision_solves) {
sparse_cholesky = FloatSuiteSparseCholesky::Create(ordering_type);
} else {
sparse_cholesky = SuiteSparseCholesky::Create(ordering_type);
}
break;
#else
LOG(FATAL) << "Ceres was compiled without support for SuiteSparse.";
#endif
case EIGEN_SPARSE:
#ifdef CERES_USE_EIGEN_SPARSE
if (options.use_mixed_precision_solves) {
sparse_cholesky = FloatEigenSparseCholesky::Create(ordering_type);
} else {
sparse_cholesky = EigenSparseCholesky::Create(ordering_type);
}
break;
#else
LOG(FATAL) << "Ceres was compiled without support for "
<< "Eigen's sparse Cholesky factorization routines.";
#endif
case CX_SPARSE:
#ifndef CERES_NO_CXSPARSE
if (options.use_mixed_precision_solves) {
sparse_cholesky = FloatCXSparseCholesky::Create(ordering_type);
} else {
sparse_cholesky = CXSparseCholesky::Create(ordering_type);
}
break;
#else
LOG(FATAL) << "Ceres was compiled without support for CXSparse.";
#endif
case ACCELERATE_SPARSE:
#ifndef CERES_NO_ACCELERATE_SPARSE
if (options.use_mixed_precision_solves) {
sparse_cholesky = AppleAccelerateCholesky<float>::Create(ordering_type);
} else {
sparse_cholesky =
AppleAccelerateCholesky<double>::Create(ordering_type);
}
break;
#else
LOG(FATAL) << "Ceres was compiled without support for Apple's Accelerate "
<< "framework solvers.";
#endif
default:
LOG(FATAL) << "Unknown sparse linear algebra library type : "
<< SparseLinearAlgebraLibraryTypeToString(
options.sparse_linear_algebra_library_type);
}
if (options.max_num_refinement_iterations > 0) {
std::unique_ptr<IterativeRefiner> refiner(
new IterativeRefiner(options.max_num_refinement_iterations));
sparse_cholesky = std::unique_ptr<SparseCholesky>(new RefinedSparseCholesky(
std::move(sparse_cholesky), std::move(refiner)));
}
return sparse_cholesky;
}
SparseCholesky::~SparseCholesky() = default;
LinearSolverTerminationType SparseCholesky::FactorAndSolve(
CompressedRowSparseMatrix* lhs,
const double* rhs,
double* solution,
std::string* message) {
LinearSolverTerminationType termination_type = Factorize(lhs, message);
if (termination_type == LINEAR_SOLVER_SUCCESS) {
termination_type = Solve(rhs, solution, message);
}
return termination_type;
}
RefinedSparseCholesky::RefinedSparseCholesky(
std::unique_ptr<SparseCholesky> sparse_cholesky,
std::unique_ptr<IterativeRefiner> iterative_refiner)
: sparse_cholesky_(std::move(sparse_cholesky)),
iterative_refiner_(std::move(iterative_refiner)) {}
RefinedSparseCholesky::~RefinedSparseCholesky() = default;
CompressedRowSparseMatrix::StorageType RefinedSparseCholesky::StorageType()
const {
return sparse_cholesky_->StorageType();
}
LinearSolverTerminationType RefinedSparseCholesky::Factorize(
CompressedRowSparseMatrix* lhs, std::string* message) {
lhs_ = lhs;
return sparse_cholesky_->Factorize(lhs, message);
}
LinearSolverTerminationType RefinedSparseCholesky::Solve(const double* rhs,
double* solution,
std::string* message) {
CHECK(lhs_ != nullptr);
auto termination_type = sparse_cholesky_->Solve(rhs, solution, message);
if (termination_type != LINEAR_SOLVER_SUCCESS) {
return termination_type;
}
iterative_refiner_->Refine(*lhs_, rhs, sparse_cholesky_.get(), solution);
return LINEAR_SOLVER_SUCCESS;
}
} // namespace internal
} // namespace ceres
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