// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 Google Inc. All rights reserved.
// http://ceres-solver.org/
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/sparse_cholesky.h"

#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() {}

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;
}

CompressedRowSparseMatrix::StorageType StorageTypeForSparseLinearAlgebraLibrary(
    SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type) {
  if (sparse_linear_algebra_library_type == SUITE_SPARSE) {
    return CompressedRowSparseMatrix::UPPER_TRIANGULAR;
  }
  return CompressedRowSparseMatrix::LOWER_TRIANGULAR;
}

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() {}

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
