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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2018 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: alexs.mac@gmail.com (Alex Stewart)
#ifndef CERES_INTERNAL_ACCELERATE_SPARSE_H_
#define CERES_INTERNAL_ACCELERATE_SPARSE_H_
// This include must come before any #ifndef check on Ceres compile options.
#include "ceres/internal/port.h"
#ifndef CERES_NO_ACCELERATE_SPARSE
#include <memory>
#include <string>
#include <vector>
#include "Accelerate.h"
#include "ceres/linear_solver.h"
#include "ceres/sparse_cholesky.h"
namespace ceres {
namespace internal {
class CompressedRowSparseMatrix;
class TripletSparseMatrix;
template <typename Scalar>
struct SparseTypesTrait {};
template <>
struct SparseTypesTrait<double> {
typedef DenseVector_Double DenseVector;
typedef SparseMatrix_Double SparseMatrix;
typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
typedef SparseOpaqueFactorization_Double NumericFactorization;
};
template <>
struct SparseTypesTrait<float> {
typedef DenseVector_Float DenseVector;
typedef SparseMatrix_Float SparseMatrix;
typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
typedef SparseOpaqueFactorization_Float NumericFactorization;
};
template <typename Scalar>
class AccelerateSparse {
public:
using DenseVector = typename SparseTypesTrait<Scalar>::DenseVector;
// Use ASSparseMatrix to avoid collision with ceres::internal::SparseMatrix.
using ASSparseMatrix = typename SparseTypesTrait<Scalar>::SparseMatrix;
using SymbolicFactorization =
typename SparseTypesTrait<Scalar>::SymbolicFactorization;
using NumericFactorization =
typename SparseTypesTrait<Scalar>::NumericFactorization;
// Solves a linear system given its symbolic (reference counted within
// NumericFactorization) and numeric factorization.
void Solve(NumericFactorization* numeric_factor,
DenseVector* rhs_and_solution);
// Note: Accelerate's API passes/returns its objects by value, but as the
// objects contain pointers to the underlying data these copies are
// all shallow (in some cases Accelerate also reference counts the
// objects internally).
ASSparseMatrix CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A);
// Computes a symbolic factorisation of A that can be used in Solve().
SymbolicFactorization AnalyzeCholesky(ASSparseMatrix* A);
// Compute the numeric Cholesky factorization of A, given its
// symbolic factorization.
NumericFactorization Cholesky(ASSparseMatrix* A,
SymbolicFactorization* symbolic_factor);
// Reuse the NumericFactorization from a previous matrix with the same
// symbolic factorization to represent a new numeric factorization.
void Cholesky(ASSparseMatrix* A, NumericFactorization* numeric_factor);
private:
std::vector<long> column_starts_;
std::vector<uint8_t> solve_workspace_;
std::vector<uint8_t> factorization_workspace_;
// Storage for the values of A if Scalar != double (necessitating a copy).
Eigen::Matrix<Scalar, Eigen::Dynamic, 1> values_;
};
// An implementation of SparseCholesky interface using Apple's Accelerate
// framework.
template <typename Scalar>
class AppleAccelerateCholesky : public SparseCholesky {
public:
// Factory
static std::unique_ptr<SparseCholesky> Create(OrderingType ordering_type);
// SparseCholesky interface.
virtual ~AppleAccelerateCholesky();
CompressedRowSparseMatrix::StorageType StorageType() const;
LinearSolverTerminationType Factorize(CompressedRowSparseMatrix* lhs,
std::string* message) final;
LinearSolverTerminationType Solve(const double* rhs,
double* solution,
std::string* message) final;
private:
AppleAccelerateCholesky(const OrderingType ordering_type);
void FreeSymbolicFactorization();
void FreeNumericFactorization();
const OrderingType ordering_type_;
AccelerateSparse<Scalar> as_;
std::unique_ptr<typename AccelerateSparse<Scalar>::SymbolicFactorization>
symbolic_factor_;
std::unique_ptr<typename AccelerateSparse<Scalar>::NumericFactorization>
numeric_factor_;
// Copy of rhs/solution if Scalar != double (necessitating a copy).
Eigen::Matrix<Scalar, Eigen::Dynamic, 1> scalar_rhs_and_solution_;
};
} // namespace internal
} // namespace ceres
#endif // CERES_NO_ACCELERATE_SPARSE
#endif // CERES_INTERNAL_ACCELERATE_SPARSE_H_
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