SimplicialCholesky_impl.h

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>

/*

NOTE: thes functions vave been adapted from the LDL library:

LDL Copyright (c) 2005 by Timothy A. Davis.  All Rights Reserved.

LDL License:

    Your use or distribution of LDL or any modified version of
    LDL implies that you agree to this License.

    This library is free software; you can redistribute it and/or
    modify it under the terms of the GNU Lesser General Public
    License as published by the Free Software Foundation; either
    version 2.1 of the License, or (at your option) any later version.

    This library is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
    Lesser General Public License for more details.

    You should have received a copy of the GNU Lesser General Public
    License along with this library; if not, write to the Free Software
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    USA

    Permission is hereby granted to use or copy this program under the
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    User documentation of any code that uses this code or any modified
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 */

#include "../Core/util/NonMPL2.h"

#ifndef EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
#define EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H

namespace Eigen {

template<typename Derived>
void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT)
{
  const StorageIndex size = StorageIndex(ap.rows());
  m_matrix.resize(size, size);
  m_parent.resize(size);
  m_nonZerosPerCol.resize(size);

  ei_declare_aligned_stack_constructed_variable(StorageIndex, tags, size, 0);

  for(StorageIndex k = 0; k < size; ++k)
  {
    /* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
    m_parent[k] = -1;             /* parent of k is not yet known */
    tags[k] = k;                  /* mark node k as visited */
    m_nonZerosPerCol[k] = 0;      /* count of nonzeros in column k of L */
    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
    {
      StorageIndex i = it.index();
      if(i < k)
      {
        /* follow path from i to root of etree, stop at flagged node */
        for(; tags[i] != k; i = m_parent[i])
        {
          /* find parent of i if not yet determined */
          if (m_parent[i] == -1)
            m_parent[i] = k;
          m_nonZerosPerCol[i]++;        /* L (k,i) is nonzero */
          tags[i] = k;                  /* mark i as visited */
        }
      }
    }
  }

  /* construct Lp index array from m_nonZerosPerCol column counts */
  StorageIndex* Lp = m_matrix.outerIndexPtr();
  Lp[0] = 0;
  for(StorageIndex k = 0; k < size; ++k)
    Lp[k+1] = Lp[k] + m_nonZerosPerCol[k] + (doLDLT ? 0 : 1);

  m_matrix.resizeNonZeros(Lp[size]);

  m_isInitialized     = true;
  m_info              = Success;
  m_analysisIsOk      = true;
  m_factorizationIsOk = false;
}


template<typename Derived>
template<bool DoLDLT>
void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType& ap)
{
  using std::sqrt;

  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
  eigen_assert(ap.rows()==ap.cols());
  eigen_assert(m_parent.size()==ap.rows());
  eigen_assert(m_nonZerosPerCol.size()==ap.rows());

  const StorageIndex size = StorageIndex(ap.rows());
  const StorageIndex* Lp = m_matrix.outerIndexPtr();
  StorageIndex* Li = m_matrix.innerIndexPtr();
  Scalar* Lx = m_matrix.valuePtr();

  ei_declare_aligned_stack_constructed_variable(Scalar, y, size, 0);
  ei_declare_aligned_stack_constructed_variable(StorageIndex,  pattern, size, 0);
  ei_declare_aligned_stack_constructed_variable(StorageIndex,  tags, size, 0);

  bool ok = true;
  m_diag.resize(DoLDLT ? size : 0);

  for(StorageIndex k = 0; k < size; ++k)
  {
    // compute nonzero pattern of kth row of L, in topological order
    y[k] = 0.0;                     // Y(0:k) is now all zero
    StorageIndex top = size;               // stack for pattern is empty
    tags[k] = k;                    // mark node k as visited
    m_nonZerosPerCol[k] = 0;        // count of nonzeros in column k of L
    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
    {
      StorageIndex i = it.index();
      if(i <= k)
      {
        y[i] += numext::conj(it.value());            /* scatter A(i,k) into Y (sum duplicates) */
        Index len;
        for(len = 0; tags[i] != k; i = m_parent[i])
        {
          pattern[len++] = i;     /* L(k,i) is nonzero */
          tags[i] = k;            /* mark i as visited */
        }
        while(len > 0)
          pattern[--top] = pattern[--len];
      }
    }

    /* compute numerical values kth row of L (a sparse triangular solve) */

    RealScalar d = numext::real(y[k]) * m_shiftScale + m_shiftOffset;    // get D(k,k), apply the shift function, and clear Y(k)
    y[k] = 0.0;
    for(; top < size; ++top)
    {
      Index i = pattern[top];       /* pattern[top:n-1] is pattern of L(:,k) */
      Scalar yi = y[i];             /* get and clear Y(i) */
      y[i] = 0.0;

      /* the nonzero entry L(k,i) */
      Scalar l_ki;
      if(DoLDLT)
        l_ki = yi / m_diag[i];
      else
        yi = l_ki = yi / Lx[Lp[i]];

      Index p2 = Lp[i] + m_nonZerosPerCol[i];
      Index p;
      for(p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p)
        y[Li[p]] -= numext::conj(Lx[p]) * yi;
      d -= numext::real(l_ki * numext::conj(yi));
      Li[p] = k;                          /* store L(k,i) in column form of L */
      Lx[p] = l_ki;
      ++m_nonZerosPerCol[i];              /* increment count of nonzeros in col i */
    }
    if(DoLDLT)
    {
      m_diag[k] = d;
      if(d == RealScalar(0))
      {
        ok = false;                         /* failure, D(k,k) is zero */
        break;
      }
    }
    else
    {
      Index p = Lp[k] + m_nonZerosPerCol[k]++;
      Li[p] = k ;                /* store L(k,k) = sqrt (d) in column k */
      if(d <= RealScalar(0)) {
        ok = false;              /* failure, matrix is not positive definite */
        break;
      }
      Lx[p] = sqrt(d) ;
    }
  }

  m_info = ok ? Success : NumericalIssue;
  m_factorizationIsOk = true;
}

} // end namespace Eigen

#endif // EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H