Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h - mirror - Git at Google

 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
 // Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // 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/.

 #ifndef EIGEN_INCOMPLETE_CHOlESKY_H
 #define EIGEN_INCOMPLETE_CHOlESKY_H

 #include <vector>
 #include <list>

 // IWYU pragma: private
 #include "./InternalHeaderCheck.h"

 namespace Eigen {
 /**
  * \brief Modified Incomplete Cholesky with dual threshold
  *
  * References : C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
  *              Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
  *
  * \tparam Scalar the scalar type of the input matrices
  * \tparam UpLo_ The triangular part that will be used for the computations. It can be Lower
  *               or Upper. Default is Lower.
  * \tparam OrderingType_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is
  * AMDOrdering<int>.
  *
  * \implsparsesolverconcept
  *
  * It performs the following incomplete factorization: \f$ S P A P' S + \sigma I \approx L L' \f$
  * where L is a lower triangular factor, S is a diagonal scaling matrix, P is a
  * fill-in reducing permutation as computed by the ordering method, and \f$ \sigma \f$ is a shift
  * for ensuring the decomposed matrix is positive definite.
  *
  * \b Shifting \b strategy: Let \f$ B = S P A P' S \f$  be the scaled matrix on which the factorization is carried out,
  * and \f$ \beta \f$ be the minimum value of the diagonal. If \f$ \beta > 0 \f$ then, the factorization is directly
  * performed on the matrix B, and \sigma = 0. Otherwise, the factorization is performed on the shifted matrix \f$ B +
  * \sigma I \f$ for a shifting factor  \f$ \sigma \f$.  We start with \f$ \sigma = \sigma_0 - \beta \f$, where \f$
  * \sigma_0 \f$ is the initial shift value as returned and set by setInitialShift() method. The default value is \f$
  * \sigma_0 = 10^{-3} \f$. If the factorization fails, then the shift in doubled until it succeed or a maximum of ten
  * attempts. If it still fails, as returned by the info() method, then you can either increase the initial shift, or
  * better use another preconditioning technique.
  *
  */
 template <typename Scalar, int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int> >
 class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar, UpLo_, OrderingType_> > {
  protected:
   typedef SparseSolverBase<IncompleteCholesky<Scalar, UpLo_, OrderingType_> > Base;
   using Base::m_isInitialized;

  public:
   typedef typename NumTraits<Scalar>::Real RealScalar;
   typedef OrderingType_ OrderingType;
   typedef typename OrderingType::PermutationType PermutationType;
   typedef typename PermutationType::StorageIndex StorageIndex;
   typedef SparseMatrix<Scalar, ColMajor, StorageIndex> FactorType;
   typedef Matrix<Scalar, Dynamic, 1> VectorSx;
   typedef Matrix<RealScalar, Dynamic, 1> VectorRx;
   typedef Matrix<StorageIndex, Dynamic, 1> VectorIx;
   typedef std::vector<std::list<StorageIndex> > VectorList;
   enum { UpLo = UpLo_ };
   enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };

  public:
   /** Default constructor leaving the object in a partly non-initialized stage.
    *
    * You must call compute() or the pair analyzePattern()/factorize() to make it valid.
    *
    * \sa IncompleteCholesky(const MatrixType&)
    */
   IncompleteCholesky() : m_initialShift(1e-3), m_analysisIsOk(false), m_factorizationIsOk(false) {}

   /** Constructor computing the incomplete factorization for the given matrix \a matrix.
    */
   template <typename MatrixType>
   IncompleteCholesky(const MatrixType& matrix)
       : m_initialShift(1e-3), m_analysisIsOk(false), m_factorizationIsOk(false) {
     compute(matrix);
   }

   /** \returns number of rows of the factored matrix */
   EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_L.rows(); }

   /** \returns number of columns of the factored matrix */
   EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_L.cols(); }

   /** \brief Reports whether previous computation was successful.
    *
    * It triggers an assertion if \c *this has not been initialized through the respective constructor,
    * or a call to compute() or analyzePattern().
    *
    * \returns \c Success if computation was successful,
    *          \c NumericalIssue if the matrix appears to be negative.
    */
   ComputationInfo info() const {
     eigen_assert(m_isInitialized && "IncompleteCholesky is not initialized.");
     return m_info;
   }

   /** \brief Set the initial shift parameter \f$ \sigma \f$.
    */
   void setInitialShift(RealScalar shift) { m_initialShift = shift; }

   /** \brief Computes the fill reducing permutation vector using the sparsity pattern of \a mat
    */
   template <typename MatrixType>
   void analyzePattern(const MatrixType& mat) {
     OrderingType ord;
     PermutationType pinv;
     ord(mat.template selfadjointView<UpLo>(), pinv);
     if (pinv.size() > 0)
       m_perm = pinv.inverse();
     else
       m_perm.resize(0);
     m_L.resize(mat.rows(), mat.cols());
     m_analysisIsOk = true;
     m_isInitialized = true;
     m_info = Success;
   }

   /** \brief Performs the numerical factorization of the input matrix \a mat
    *
    * The method analyzePattern() or compute() must have been called beforehand
    * with a matrix having the same pattern.
    *
    * \sa compute(), analyzePattern()
    */
   template <typename MatrixType>
   void factorize(const MatrixType& mat);

   /** Computes or re-computes the incomplete Cholesky factorization of the input matrix \a mat
    *
    * It is a shortcut for a sequential call to the analyzePattern() and factorize() methods.
    *
    * \sa analyzePattern(), factorize()
    */
   template <typename MatrixType>
   void compute(const MatrixType& mat) {
     analyzePattern(mat);
     factorize(mat);
   }

   // internal
   template <typename Rhs, typename Dest>
   void _solve_impl(const Rhs& b, Dest& x) const {
     eigen_assert(m_factorizationIsOk && "factorize() should be called first");
     if (m_perm.rows() == b.rows())
       x = m_perm * b;
     else
       x = b;
     x = m_scale.asDiagonal() * x;
     x = m_L.template triangularView<Lower>().solve(x);
     x = m_L.adjoint().template triangularView<Upper>().solve(x);
     x = m_scale.asDiagonal() * x;
     if (m_perm.rows() == b.rows()) x = m_perm.inverse() * x;
   }

   /** \returns the sparse lower triangular factor L */
   const FactorType& matrixL() const {
     eigen_assert(m_factorizationIsOk && "factorize() should be called first");
     return m_L;
   }

   /** \returns a vector representing the scaling factor S */
   const VectorRx& scalingS() const {
     eigen_assert(m_factorizationIsOk && "factorize() should be called first");
     return m_scale;
   }

   /** \returns the fill-in reducing permutation P (can be empty for a natural ordering) */
   const PermutationType& permutationP() const {
     eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
     return m_perm;
   }

   /** \returns the final shift parameter from the computation */
   RealScalar shift() const { return m_shift; }

  protected:
   FactorType m_L;             // The lower part stored in CSC
   VectorRx m_scale;           // The vector for scaling the matrix
   RealScalar m_initialShift;  // The initial shift parameter
   bool m_analysisIsOk;
   bool m_factorizationIsOk;
   ComputationInfo m_info;
   PermutationType m_perm;
   RealScalar m_shift;  // The final shift parameter.

  private:
   inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col,
                          const Index& jk, VectorIx& firstElt, VectorList& listCol);
 };

 // Based on the following paper:
 //   C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
 //   Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
 //   http://ftp.mcs.anl.gov/pub/tech_reports/reports/P682.pdf
 template <typename Scalar, int UpLo_, typename OrderingType>
 template <typename MatrixType_>
 void IncompleteCholesky<Scalar, UpLo_, OrderingType>::factorize(const MatrixType_& mat) {
   using std::sqrt;
   eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");

   // Dropping strategy : Keep only the p largest elements per column, where p is the number of elements in the column of
   // the original matrix. Other strategies will be added

   // Apply the fill-reducing permutation computed in analyzePattern()
   if (m_perm.rows() == mat.rows())  // To detect the null permutation
   {
     // The temporary is needed to make sure that the diagonal entry is properly sorted
     FactorType tmp(mat.rows(), mat.cols());
     tmp = mat.template selfadjointView<UpLo_>().twistedBy(m_perm);
     m_L.template selfadjointView<Lower>() = tmp.template selfadjointView<Lower>();
   } else {
     m_L.template selfadjointView<Lower>() = mat.template selfadjointView<UpLo_>();
   }

   // The algorithm will insert increasingly large shifts on the diagonal until
   // factorization succeeds. Therefore we have to make sure that there is a
   // space in the datastructure to store such values, even if the original
   // matrix has a zero on the diagonal.
   bool modified = false;
   for (Index i = 0; i < mat.cols(); ++i) {
     bool inserted = false;
     m_L.findOrInsertCoeff(i, i, &inserted);
     if (inserted) {
       modified = true;
     }
   }
   if (modified) m_L.makeCompressed();

   Index n = m_L.cols();
   Index nnz = m_L.nonZeros();
   Map<VectorSx> vals(m_L.valuePtr(), nnz);           // values
   Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz);    // Row indices
   Map<VectorIx> colPtr(m_L.outerIndexPtr(), n + 1);  // Pointer to the beginning of each row
   VectorIx firstElt(n - 1);  // for each j, points to the next entry in vals that will be used in the factorization
   VectorList listCol(n);     // listCol(j) is a linked list of columns to update column j
   VectorSx col_vals(n);      // Store a  nonzero values in each column
   VectorIx col_irow(n);      // Row indices of nonzero elements in each column
   VectorIx col_pattern(n);
   col_pattern.fill(-1);
   StorageIndex col_nnz;

   // Computes the scaling factors
   m_scale.resize(n);
   m_scale.setZero();
   for (Index j = 0; j < n; j++)
     for (Index k = colPtr[j]; k < colPtr[j + 1]; k++) {
       m_scale(j) += numext::abs2(vals(k));
       if (rowIdx[k] != j) m_scale(rowIdx[k]) += numext::abs2(vals(k));
     }

   m_scale = m_scale.cwiseSqrt().cwiseSqrt();

   for (Index j = 0; j < n; ++j)
     if (m_scale(j) > (std::numeric_limits<RealScalar>::min)())
       m_scale(j) = RealScalar(1) / m_scale(j);
     else
       m_scale(j) = 1;

   // TODO disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)

   // Scale and compute the shift for the matrix
   RealScalar mindiag = NumTraits<RealScalar>::highest();
   for (Index j = 0; j < n; j++) {
     for (Index k = colPtr[j]; k < colPtr[j + 1]; k++) vals[k] *= (m_scale(j) * m_scale(rowIdx[k]));
     eigen_internal_assert(rowIdx[colPtr[j]] == j &&
                           "IncompleteCholesky: only the lower triangular part must be stored");
     mindiag = numext::mini(numext::real(vals[colPtr[j]]), mindiag);
   }

   FactorType L_save = m_L;

   m_shift = RealScalar(0);
   if (mindiag <= RealScalar(0.)) m_shift = m_initialShift - mindiag;

   m_info = NumericalIssue;

   // Try to perform the incomplete factorization using the current shift
   int iter = 0;
   do {
     // Apply the shift to the diagonal elements of the matrix
     for (Index j = 0; j < n; j++) vals[colPtr[j]] += m_shift;

     // jki version of the Cholesky factorization
     Index j = 0;
     for (; j < n; ++j) {
       // Left-looking factorization of the j-th column
       // First, load the j-th column into col_vals
       Scalar diag = vals[colPtr[j]];  // It is assumed that only the lower part is stored
       col_nnz = 0;
       for (Index i = colPtr[j] + 1; i < colPtr[j + 1]; i++) {
         StorageIndex l = rowIdx[i];
         col_vals(col_nnz) = vals[i];
         col_irow(col_nnz) = l;
         col_pattern(l) = col_nnz;
         col_nnz++;
       }
       {
         typename std::list<StorageIndex>::iterator k;
         // Browse all previous columns that will update column j
         for (k = listCol[j].begin(); k != listCol[j].end(); k++) {
           Index jk = firstElt(*k);  // First element to use in the column
           eigen_internal_assert(rowIdx[jk] == j);
           Scalar v_j_jk = numext::conj(vals[jk]);

           jk += 1;
           for (Index i = jk; i < colPtr[*k + 1]; i++) {
             StorageIndex l = rowIdx[i];
             if (col_pattern[l] < 0) {
               col_vals(col_nnz) = vals[i] * v_j_jk;
               col_irow[col_nnz] = l;
               col_pattern(l) = col_nnz;
               col_nnz++;
             } else
               col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
           }
           updateList(colPtr, rowIdx, vals, *k, jk, firstElt, listCol);
         }
       }

       // Scale the current column
       if (numext::real(diag) <= 0) {
         if (++iter >= 10) return;

         // increase shift
         m_shift = numext::maxi(m_initialShift, RealScalar(2) * m_shift);
         // restore m_L, col_pattern, and listCol
         vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
         rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
         colPtr = Map<const VectorIx>(L_save.outerIndexPtr(), n + 1);
         col_pattern.fill(-1);
         for (Index i = 0; i < n; ++i) listCol[i].clear();

         break;
       }

       RealScalar rdiag = sqrt(numext::real(diag));
       vals[colPtr[j]] = rdiag;
       for (Index k = 0; k < col_nnz; ++k) {
         Index i = col_irow[k];
         // Scale
         col_vals(k) /= rdiag;
         // Update the remaining diagonals with col_vals
         vals[colPtr[i]] -= numext::abs2(col_vals(k));
       }
       // Select the largest p elements
       // p is the original number of elements in the column (without the diagonal)
       Index p = colPtr[j + 1] - colPtr[j] - 1;
       Ref<VectorSx> cvals = col_vals.head(col_nnz);
       Ref<VectorIx> cirow = col_irow.head(col_nnz);
       internal::QuickSplit(cvals, cirow, p);
       // Insert the largest p elements in the matrix
       Index cpt = 0;
       for (Index i = colPtr[j] + 1; i < colPtr[j + 1]; i++) {
         vals[i] = col_vals(cpt);
         rowIdx[i] = col_irow(cpt);
         // restore col_pattern:
         col_pattern(col_irow(cpt)) = -1;
         cpt++;
       }
       // Get the first smallest row index and put it after the diagonal element
       Index jk = colPtr(j) + 1;
       updateList(colPtr, rowIdx, vals, j, jk, firstElt, listCol);
     }

     if (j == n) {
       m_factorizationIsOk = true;
       m_info = Success;
     }
   } while (m_info != Success);
 }

 template <typename Scalar, int UpLo_, typename OrderingType>
 inline void IncompleteCholesky<Scalar, UpLo_, OrderingType>::updateList(Ref<const VectorIx> colPtr,
                                                                         Ref<VectorIx> rowIdx, Ref<VectorSx> vals,
                                                                         const Index& col, const Index& jk,
                                                                         VectorIx& firstElt, VectorList& listCol) {
   if (jk < colPtr(col + 1)) {
     Index p = colPtr(col + 1) - jk;
     Index minpos;
     rowIdx.segment(jk, p).minCoeff(&minpos);
     minpos += jk;
     if (rowIdx(minpos) != rowIdx(jk)) {
       // Swap
       std::swap(rowIdx(jk), rowIdx(minpos));
       std::swap(vals(jk), vals(minpos));
     }
     firstElt(col) = internal::convert_index<StorageIndex, Index>(jk);
     listCol[rowIdx(jk)].push_back(internal::convert_index<StorageIndex, Index>(col));
   }
 }

 }  // end namespace Eigen

 #endif
	// This file is part of Eigen, a lightweight C++ template library
	// for linear algebra.
	//
	// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
	// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
	//
	// 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/.

	#ifndef EIGEN_INCOMPLETE_CHOlESKY_H
	#define EIGEN_INCOMPLETE_CHOlESKY_H

	#include <vector>
	#include <list>

	// IWYU pragma: private
	#include "./InternalHeaderCheck.h"

	namespace Eigen {
	/**
	* \brief Modified Incomplete Cholesky with dual threshold
	*
	* References : C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
	* Limited memory, SIAM J. Sci. Comput. 21(1), pp. 24-45, 1999
	*
	* \tparam Scalar the scalar type of the input matrices
	* \tparam UpLo_ The triangular part that will be used for the computations. It can be Lower
	* or Upper. Default is Lower.
	* \tparam OrderingType_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is
	* AMDOrdering<int>.
	*
	* \implsparsesolverconcept
	*
	* It performs the following incomplete factorization: \f$ S P A P' S + \sigma I \approx L L' \f$
	* where L is a lower triangular factor, S is a diagonal scaling matrix, P is a
	* fill-in reducing permutation as computed by the ordering method, and \f$ \sigma \f$ is a shift
	* for ensuring the decomposed matrix is positive definite.
	*
	* \b Shifting \b strategy: Let \f$ B = S P A P' S \f$ be the scaled matrix on which the factorization is carried out,
	* and \f$ \beta \f$ be the minimum value of the diagonal. If \f$ \beta > 0 \f$ then, the factorization is directly
	* performed on the matrix B, and \sigma = 0. Otherwise, the factorization is performed on the shifted matrix \f$ B +
	* \sigma I \f$ for a shifting factor \f$ \sigma \f$. We start with \f$ \sigma = \sigma_0 - \beta \f$, where \f$
	* \sigma_0 \f$ is the initial shift value as returned and set by setInitialShift() method. The default value is \f$
	* \sigma_0 = 10^{-3} \f$. If the factorization fails, then the shift in doubled until it succeed or a maximum of ten
	* attempts. If it still fails, as returned by the info() method, then you can either increase the initial shift, or
	* better use another preconditioning technique.
	*
	*/
	template <typename Scalar, int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int> >
	class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar, UpLo_, OrderingType_> > {
	protected:
	typedef SparseSolverBase<IncompleteCholesky<Scalar, UpLo_, OrderingType_> > Base;
	using Base::m_isInitialized;

	public:
	typedef typename NumTraits<Scalar>::Real RealScalar;
	typedef OrderingType_ OrderingType;
	typedef typename OrderingType::PermutationType PermutationType;
	typedef typename PermutationType::StorageIndex StorageIndex;
	typedef SparseMatrix<Scalar, ColMajor, StorageIndex> FactorType;
	typedef Matrix<Scalar, Dynamic, 1> VectorSx;
	typedef Matrix<RealScalar, Dynamic, 1> VectorRx;
	typedef Matrix<StorageIndex, Dynamic, 1> VectorIx;
	typedef std::vector<std::list<StorageIndex> > VectorList;
	enum { UpLo = UpLo_ };
	enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };

	public:
	/** Default constructor leaving the object in a partly non-initialized stage.
	*
	* You must call compute() or the pair analyzePattern()/factorize() to make it valid.
	*
	* \sa IncompleteCholesky(const MatrixType&)
	*/
	IncompleteCholesky() : m_initialShift(1e-3), m_analysisIsOk(false), m_factorizationIsOk(false) {}

	/** Constructor computing the incomplete factorization for the given matrix \a matrix.
	*/
	template <typename MatrixType>
	IncompleteCholesky(const MatrixType& matrix)
	: m_initialShift(1e-3), m_analysisIsOk(false), m_factorizationIsOk(false) {
	compute(matrix);
	}

	/** \returns number of rows of the factored matrix */
	EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_L.rows(); }

	/** \returns number of columns of the factored matrix */
	EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_L.cols(); }

	/** \brief Reports whether previous computation was successful.
	*
	* It triggers an assertion if \c *this has not been initialized through the respective constructor,
	* or a call to compute() or analyzePattern().
	*
	* \returns \c Success if computation was successful,
	* \c NumericalIssue if the matrix appears to be negative.
	*/
	ComputationInfo info() const {
	eigen_assert(m_isInitialized && "IncompleteCholesky is not initialized.");
	return m_info;
	}

	/** \brief Set the initial shift parameter \f$ \sigma \f$.
	*/
	void setInitialShift(RealScalar shift) { m_initialShift = shift; }

	/** \brief Computes the fill reducing permutation vector using the sparsity pattern of \a mat
	*/
	template <typename MatrixType>
	void analyzePattern(const MatrixType& mat) {
	OrderingType ord;
	PermutationType pinv;
	ord(mat.template selfadjointView<UpLo>(), pinv);
	if (pinv.size() > 0)
	m_perm = pinv.inverse();
	else
	m_perm.resize(0);
	m_L.resize(mat.rows(), mat.cols());
	m_analysisIsOk = true;
	m_isInitialized = true;
	m_info = Success;
	}

	/** \brief Performs the numerical factorization of the input matrix \a mat
	*
	* The method analyzePattern() or compute() must have been called beforehand
	* with a matrix having the same pattern.
	*
	* \sa compute(), analyzePattern()
	*/
	template <typename MatrixType>
	void factorize(const MatrixType& mat);

	/** Computes or re-computes the incomplete Cholesky factorization of the input matrix \a mat
	*
	* It is a shortcut for a sequential call to the analyzePattern() and factorize() methods.
	*
	* \sa analyzePattern(), factorize()
	*/
	template <typename MatrixType>
	void compute(const MatrixType& mat) {
	analyzePattern(mat);
	factorize(mat);
	}

	// internal
	template <typename Rhs, typename Dest>
	void _solve_impl(const Rhs& b, Dest& x) const {
	eigen_assert(m_factorizationIsOk && "factorize() should be called first");
	if (m_perm.rows() == b.rows())
	x = m_perm * b;
	else
	x = b;
	x = m_scale.asDiagonal() * x;
	x = m_L.template triangularView<Lower>().solve(x);
	x = m_L.adjoint().template triangularView<Upper>().solve(x);
	x = m_scale.asDiagonal() * x;
	if (m_perm.rows() == b.rows()) x = m_perm.inverse() * x;
	}

	/** \returns the sparse lower triangular factor L */
	const FactorType& matrixL() const {
	eigen_assert(m_factorizationIsOk && "factorize() should be called first");
	return m_L;
	}

	/** \returns a vector representing the scaling factor S */
	const VectorRx& scalingS() const {
	eigen_assert(m_factorizationIsOk && "factorize() should be called first");
	return m_scale;
	}

	/** \returns the fill-in reducing permutation P (can be empty for a natural ordering) */
	const PermutationType& permutationP() const {
	eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
	return m_perm;
	}

	/** \returns the final shift parameter from the computation */
	RealScalar shift() const { return m_shift; }

	protected:
	FactorType m_L; // The lower part stored in CSC
	VectorRx m_scale; // The vector for scaling the matrix
	RealScalar m_initialShift; // The initial shift parameter
	bool m_analysisIsOk;
	bool m_factorizationIsOk;
	ComputationInfo m_info;
	PermutationType m_perm;
	RealScalar m_shift; // The final shift parameter.

	private:
	inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col,
	const Index& jk, VectorIx& firstElt, VectorList& listCol);
	};

	// Based on the following paper:
	// C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
	// Limited memory, SIAM J. Sci. Comput. 21(1), pp. 24-45, 1999
	// http://ftp.mcs.anl.gov/pub/tech_reports/reports/P682.pdf
	template <typename Scalar, int UpLo_, typename OrderingType>
	template <typename MatrixType_>
	void IncompleteCholesky<Scalar, UpLo_, OrderingType>::factorize(const MatrixType_& mat) {
	using std::sqrt;
	eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");

	// Dropping strategy : Keep only the p largest elements per column, where p is the number of elements in the column of
	// the original matrix. Other strategies will be added

	// Apply the fill-reducing permutation computed in analyzePattern()
	if (m_perm.rows() == mat.rows()) // To detect the null permutation
	{
	// The temporary is needed to make sure that the diagonal entry is properly sorted
	FactorType tmp(mat.rows(), mat.cols());
	tmp = mat.template selfadjointView<UpLo_>().twistedBy(m_perm);
	m_L.template selfadjointView<Lower>() = tmp.template selfadjointView<Lower>();
	} else {
	m_L.template selfadjointView<Lower>() = mat.template selfadjointView<UpLo_>();
	}

	// The algorithm will insert increasingly large shifts on the diagonal until
	// factorization succeeds. Therefore we have to make sure that there is a
	// space in the datastructure to store such values, even if the original
	// matrix has a zero on the diagonal.
	bool modified = false;
	for (Index i = 0; i < mat.cols(); ++i) {
	bool inserted = false;
	m_L.findOrInsertCoeff(i, i, &inserted);
	if (inserted) {
	modified = true;
	}
	}
	if (modified) m_L.makeCompressed();

	Index n = m_L.cols();
	Index nnz = m_L.nonZeros();
	Map<VectorSx> vals(m_L.valuePtr(), nnz); // values
	Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz); // Row indices
	Map<VectorIx> colPtr(m_L.outerIndexPtr(), n + 1); // Pointer to the beginning of each row
	VectorIx firstElt(n - 1); // for each j, points to the next entry in vals that will be used in the factorization
	VectorList listCol(n); // listCol(j) is a linked list of columns to update column j
	VectorSx col_vals(n); // Store a nonzero values in each column
	VectorIx col_irow(n); // Row indices of nonzero elements in each column
	VectorIx col_pattern(n);
	col_pattern.fill(-1);
	StorageIndex col_nnz;

	// Computes the scaling factors
	m_scale.resize(n);
	m_scale.setZero();
	for (Index j = 0; j < n; j++)
	for (Index k = colPtr[j]; k < colPtr[j + 1]; k++) {
	m_scale(j) += numext::abs2(vals(k));
	if (rowIdx[k] != j) m_scale(rowIdx[k]) += numext::abs2(vals(k));
	}

	m_scale = m_scale.cwiseSqrt().cwiseSqrt();

	for (Index j = 0; j < n; ++j)
	if (m_scale(j) > (std::numeric_limits<RealScalar>::min)())
	m_scale(j) = RealScalar(1) / m_scale(j);
	else
	m_scale(j) = 1;

	// TODO disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)

	// Scale and compute the shift for the matrix
	RealScalar mindiag = NumTraits<RealScalar>::highest();
	for (Index j = 0; j < n; j++) {
	for (Index k = colPtr[j]; k < colPtr[j + 1]; k++) vals[k] = (m_scale(j) m_scale(rowIdx[k]));
	eigen_internal_assert(rowIdx[colPtr[j]] == j &&
	"IncompleteCholesky: only the lower triangular part must be stored");
	mindiag = numext::mini(numext::real(vals[colPtr[j]]), mindiag);
	}

	FactorType L_save = m_L;

	m_shift = RealScalar(0);
	if (mindiag <= RealScalar(0.)) m_shift = m_initialShift - mindiag;

	m_info = NumericalIssue;

	// Try to perform the incomplete factorization using the current shift
	int iter = 0;
	do {
	// Apply the shift to the diagonal elements of the matrix
	for (Index j = 0; j < n; j++) vals[colPtr[j]] += m_shift;

	// jki version of the Cholesky factorization
	Index j = 0;
	for (; j < n; ++j) {
	// Left-looking factorization of the j-th column
	// First, load the j-th column into col_vals
	Scalar diag = vals[colPtr[j]]; // It is assumed that only the lower part is stored
	col_nnz = 0;
	for (Index i = colPtr[j] + 1; i < colPtr[j + 1]; i++) {
	StorageIndex l = rowIdx[i];
	col_vals(col_nnz) = vals[i];
	col_irow(col_nnz) = l;
	col_pattern(l) = col_nnz;
	col_nnz++;
	}
	{
	typename std::list<StorageIndex>::iterator k;
	// Browse all previous columns that will update column j
	for (k = listCol[j].begin(); k != listCol[j].end(); k++) {
	Index jk = firstElt(*k); // First element to use in the column
	eigen_internal_assert(rowIdx[jk] == j);
	Scalar v_j_jk = numext::conj(vals[jk]);

	jk += 1;
	for (Index i = jk; i < colPtr[*k + 1]; i++) {
	StorageIndex l = rowIdx[i];
	if (col_pattern[l] < 0) {
	col_vals(col_nnz) = vals[i] * v_j_jk;
	col_irow[col_nnz] = l;
	col_pattern(l) = col_nnz;
	col_nnz++;
	} else
	col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
	}
	updateList(colPtr, rowIdx, vals, *k, jk, firstElt, listCol);
	}
	}

	// Scale the current column
	if (numext::real(diag) <= 0) {
	if (++iter >= 10) return;

	// increase shift
	m_shift = numext::maxi(m_initialShift, RealScalar(2) * m_shift);
	// restore m_L, col_pattern, and listCol
	vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
	rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
	colPtr = Map<const VectorIx>(L_save.outerIndexPtr(), n + 1);
	col_pattern.fill(-1);
	for (Index i = 0; i < n; ++i) listCol[i].clear();

	break;
	}

	RealScalar rdiag = sqrt(numext::real(diag));
	vals[colPtr[j]] = rdiag;
	for (Index k = 0; k < col_nnz; ++k) {
	Index i = col_irow[k];
	// Scale
	col_vals(k) /= rdiag;
	// Update the remaining diagonals with col_vals
	vals[colPtr[i]] -= numext::abs2(col_vals(k));
	}
	// Select the largest p elements
	// p is the original number of elements in the column (without the diagonal)
	Index p = colPtr[j + 1] - colPtr[j] - 1;
	Ref<VectorSx> cvals = col_vals.head(col_nnz);
	Ref<VectorIx> cirow = col_irow.head(col_nnz);
	internal::QuickSplit(cvals, cirow, p);
	// Insert the largest p elements in the matrix
	Index cpt = 0;
	for (Index i = colPtr[j] + 1; i < colPtr[j + 1]; i++) {
	vals[i] = col_vals(cpt);
	rowIdx[i] = col_irow(cpt);
	// restore col_pattern:
	col_pattern(col_irow(cpt)) = -1;
	cpt++;
	}
	// Get the first smallest row index and put it after the diagonal element
	Index jk = colPtr(j) + 1;
	updateList(colPtr, rowIdx, vals, j, jk, firstElt, listCol);
	}

	if (j == n) {
	m_factorizationIsOk = true;
	m_info = Success;
	}
	} while (m_info != Success);
	}

	template <typename Scalar, int UpLo_, typename OrderingType>
	inline void IncompleteCholesky<Scalar, UpLo_, OrderingType>::updateList(Ref<const VectorIx> colPtr,
	Ref<VectorIx> rowIdx, Ref<VectorSx> vals,
	const Index& col, const Index& jk,
	VectorIx& firstElt, VectorList& listCol) {
	if (jk < colPtr(col + 1)) {
	Index p = colPtr(col + 1) - jk;
	Index minpos;
	rowIdx.segment(jk, p).minCoeff(&minpos);
	minpos += jk;
	if (rowIdx(minpos) != rowIdx(jk)) {
	// Swap
	std::swap(rowIdx(jk), rowIdx(minpos));
	std::swap(vals(jk), vals(minpos));
	}
	firstElt(col) = internal::convert_index<StorageIndex, Index>(jk);
	listCol[rowIdx(jk)].push_back(internal::convert_index<StorageIndex, Index>(col));
	}
	}

	} // end namespace Eigen

	#endif