KLUSupport.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2017 Kyle Macfarlan <kyle.macfarlan@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// SPDX-License-Identifier: MPL-2.0
 
#ifndef EIGEN_KLUSUPPORT_H
#define EIGEN_KLUSUPPORT_H
 
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
 
namespace Eigen {
 
/* TODO extract L, extract U, compute det, etc... */

 
inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B[],
                     klu_common *Common, double) {
  return klu_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B,
                   Common);
}
 
inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double> B[],
                     klu_common *Common, std::complex<double>) {
  return klu_z_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs),
                     &numext::real_ref(B[0]), Common);
}
 
inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B[],
                      klu_common *Common, double) {
  return klu_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B,
                    Common);
}
 
inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double> B[],
                      klu_common *Common, std::complex<double>) {
  return klu_z_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs),
                      &numext::real_ref(B[0]), 0, Common);
}
 
inline klu_numeric *klu_factor(int Ap[], int Ai[], double Ax[], klu_symbolic *Symbolic, klu_common *Common, double) {
  return klu_factor(Ap, Ai, Ax, Symbolic, Common);
}
 
inline klu_numeric *klu_factor(int Ap[], int Ai[], std::complex<double> Ax[], klu_symbolic *Symbolic,
                               klu_common *Common, std::complex<double>) {
  return klu_z_factor(Ap, Ai, &numext::real_ref(Ax[0]), Symbolic, Common);
}
 
template <typename MatrixType_>
class KLU : public SparseSolverBase<KLU<MatrixType_> > {
 protected:
  typedef SparseSolverBase<KLU<MatrixType_> > Base;
  using Base::m_isInitialized;
 
 public:
  using Base::_solve_impl;
  typedef MatrixType_ MatrixType;
  typedef typename MatrixType::Scalar Scalar;
  typedef typename MatrixType::RealScalar RealScalar;
  typedef typename MatrixType::StorageIndex StorageIndex;
  typedef Matrix<Scalar, Dynamic, 1> Vector;
  typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
  typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
  typedef SparseMatrix<Scalar> LUMatrixType;
  typedef SparseMatrix<Scalar, ColMajor, int> KLUMatrixType;
  typedef Ref<const KLUMatrixType, StandardCompressedFormat> KLUMatrixRef;
  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
 
 public:
  KLU() : m_dummy(0, 0), mp_matrix(m_dummy) { init(); }
 
  template <typename InputMatrixType>
  explicit KLU(const InputMatrixType &matrix) : mp_matrix(matrix) {
    init();
    compute(matrix);
  }
 
  ~KLU() {
    if (m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
    if (m_numeric) klu_free_numeric(&m_numeric, &m_common);
  }
 
  constexpr Index rows() const noexcept { return mp_matrix.rows(); }
  constexpr Index cols() const noexcept { return mp_matrix.cols(); }

  ComputationInfo info() const {
    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
    return m_info;
  }
  template <typename InputMatrixType>
  void compute(const InputMatrixType &matrix) {
    if (m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
    if (m_numeric) klu_free_numeric(&m_numeric, &m_common);
    grab(matrix.derived());
    analyzePattern_impl();
    factorize_impl();
  }

  template <typename InputMatrixType>
  void analyzePattern(const InputMatrixType &matrix) {
    if (m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
    if (m_numeric) klu_free_numeric(&m_numeric, &m_common);
 
    grab(matrix.derived());
 
    analyzePattern_impl();
  }

  inline const klu_common &kluCommon() const { return m_common; }

  inline klu_common &kluCommon() { return m_common; }

  template <typename InputMatrixType>
  void factorize(const InputMatrixType &matrix) {
    eigen_assert(m_analysisIsOk && "KLU: you must first call analyzePattern()");
    if (m_numeric) klu_free_numeric(&m_numeric, &m_common);
 
    grab(matrix.derived());
 
    factorize_impl();
  }

  template <typename BDerived, typename XDerived>
  bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
 
 protected:
  void init() {
    m_info = InvalidInput;
    m_isInitialized = false;
    m_numeric = 0;
    m_symbolic = 0;
    m_extractedDataAreDirty = true;
 
    klu_defaults(&m_common);
  }
 
  void analyzePattern_impl() {
    m_info = InvalidInput;
    m_analysisIsOk = false;
    m_factorizationIsOk = false;
    m_symbolic = klu_analyze(internal::convert_index<int>(mp_matrix.rows()),
                             const_cast<StorageIndex *>(mp_matrix.outerIndexPtr()),
                             const_cast<StorageIndex *>(mp_matrix.innerIndexPtr()), &m_common);
    if (m_symbolic) {
      m_isInitialized = true;
      m_info = Success;
      m_analysisIsOk = true;
      m_extractedDataAreDirty = true;
    }
  }
 
  void factorize_impl() {
    m_numeric = klu_factor(const_cast<StorageIndex *>(mp_matrix.outerIndexPtr()),
                           const_cast<StorageIndex *>(mp_matrix.innerIndexPtr()),
                           const_cast<Scalar *>(mp_matrix.valuePtr()), m_symbolic, &m_common, Scalar());
 
    m_info = m_numeric ? Success : NumericalIssue;
    m_factorizationIsOk = m_numeric ? 1 : 0;
    m_extractedDataAreDirty = true;
  }
 
  template <typename MatrixDerived>
  void grab(const EigenBase<MatrixDerived> &A) {
    internal::destroy_at(&mp_matrix);
    internal::construct_at(&mp_matrix, A.derived());
  }
 
  void grab(const KLUMatrixRef &A) {
    if (&(A.derived()) != &mp_matrix) {
      internal::destroy_at(&mp_matrix);
      internal::construct_at(&mp_matrix, A);
    }
  }
 
  KLUMatrixType m_dummy;
  KLUMatrixRef mp_matrix;
 
  klu_numeric *m_numeric;
  klu_symbolic *m_symbolic;
  klu_common m_common;
  mutable ComputationInfo m_info;
  int m_factorizationIsOk;
  int m_analysisIsOk;
  mutable bool m_extractedDataAreDirty;
 
 private:
  KLU(const KLU &) {}
};
 
template <typename MatrixType>
template <typename BDerived, typename XDerived>
bool KLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const {
  Index rhsCols = b.cols();
  EIGEN_STATIC_ASSERT((XDerived::Flags & RowMajorBit) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
  eigen_assert(m_factorizationIsOk &&
               "The decomposition is not in a valid state for solving, you must first call either compute() or "
               "analyzePattern()/factorize()");
 
  x = b;
  int info = klu_solve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(),
                       const_cast<klu_common *>(&m_common), Scalar());
 
  m_info = info != 0 ? Success : NumericalIssue;
  return true;
}
 
}  // end namespace Eigen
 
#endif  // EIGEN_KLUSUPPORT_H