Squashed 'third_party/eigen/' content from commit 61d72f6
Change-Id: Iccc90fa0b55ab44037f018046d2fcffd90d9d025
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git-subtree-split: 61d72f6383cfa842868c53e30e087b0258177257
diff --git a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
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+// 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>
+//
+// 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_LUT_H
+#define EIGEN_INCOMPLETE_LUT_H
+
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * Compute a quick-sort split of a vector
+ * On output, the vector row is permuted such that its elements satisfy
+ * abs(row(i)) >= abs(row(ncut)) if i<ncut
+ * abs(row(i)) <= abs(row(ncut)) if i>ncut
+ * \param row The vector of values
+ * \param ind The array of index for the elements in @p row
+ * \param ncut The number of largest elements to keep
+ **/
+template <typename VectorV, typename VectorI, typename Index>
+Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
+{
+ typedef typename VectorV::RealScalar RealScalar;
+ using std::swap;
+ using std::abs;
+ Index mid;
+ Index n = row.size(); /* length of the vector */
+ Index first, last ;
+
+ ncut--; /* to fit the zero-based indices */
+ first = 0;
+ last = n-1;
+ if (ncut < first || ncut > last ) return 0;
+
+ do {
+ mid = first;
+ RealScalar abskey = abs(row(mid));
+ for (Index j = first + 1; j <= last; j++) {
+ if ( abs(row(j)) > abskey) {
+ ++mid;
+ swap(row(mid), row(j));
+ swap(ind(mid), ind(j));
+ }
+ }
+ /* Interchange for the pivot element */
+ swap(row(mid), row(first));
+ swap(ind(mid), ind(first));
+
+ if (mid > ncut) last = mid - 1;
+ else if (mid < ncut ) first = mid + 1;
+ } while (mid != ncut );
+
+ return 0; /* mid is equal to ncut */
+}
+
+}// end namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+ * \class IncompleteLUT
+ * \brief Incomplete LU factorization with dual-threshold strategy
+ *
+ * During the numerical factorization, two dropping rules are used :
+ * 1) any element whose magnitude is less than some tolerance is dropped.
+ * This tolerance is obtained by multiplying the input tolerance @p droptol
+ * by the average magnitude of all the original elements in the current row.
+ * 2) After the elimination of the row, only the @p fill largest elements in
+ * the L part and the @p fill largest elements in the U part are kept
+ * (in addition to the diagonal element ). Note that @p fill is computed from
+ * the input parameter @p fillfactor which is used the ratio to control the fill_in
+ * relatively to the initial number of nonzero elements.
+ *
+ * The two extreme cases are when @p droptol=0 (to keep all the @p fill*2 largest elements)
+ * and when @p fill=n/2 with @p droptol being different to zero.
+ *
+ * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization,
+ * Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994.
+ *
+ * NOTE : The following implementation is derived from the ILUT implementation
+ * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota
+ * released under the terms of the GNU LGPL:
+ * http://www-users.cs.umn.edu/~saad/software/SPARSKIT/README
+ * However, Yousef Saad gave us permission to relicense his ILUT code to MPL2.
+ * See the Eigen mailing list archive, thread: ILUT, date: July 8, 2012:
+ * http://listengine.tuxfamily.org/lists.tuxfamily.org/eigen/2012/07/msg00064.html
+ * alternatively, on GMANE:
+ * http://comments.gmane.org/gmane.comp.lib.eigen/3302
+ */
+template <typename _Scalar>
+class IncompleteLUT : internal::noncopyable
+{
+ typedef _Scalar Scalar;
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+ typedef Matrix<Scalar,Dynamic,1> Vector;
+ typedef SparseMatrix<Scalar,RowMajor> FactorType;
+ typedef SparseMatrix<Scalar,ColMajor> PermutType;
+ typedef typename FactorType::Index Index;
+
+ public:
+ typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
+
+ IncompleteLUT()
+ : m_droptol(NumTraits<Scalar>::dummy_precision()), m_fillfactor(10),
+ m_analysisIsOk(false), m_factorizationIsOk(false), m_isInitialized(false)
+ {}
+
+ template<typename MatrixType>
+ IncompleteLUT(const MatrixType& mat, const RealScalar& droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10)
+ : m_droptol(droptol),m_fillfactor(fillfactor),
+ m_analysisIsOk(false),m_factorizationIsOk(false),m_isInitialized(false)
+ {
+ eigen_assert(fillfactor != 0);
+ compute(mat);
+ }
+
+ Index rows() const { return m_lu.rows(); }
+
+ Index cols() const { return m_lu.cols(); }
+
+ /** \brief Reports whether previous computation was successful.
+ *
+ * \returns \c Success if computation was succesful,
+ * \c NumericalIssue if the matrix.appears to be negative.
+ */
+ ComputationInfo info() const
+ {
+ eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
+ return m_info;
+ }
+
+ template<typename MatrixType>
+ void analyzePattern(const MatrixType& amat);
+
+ template<typename MatrixType>
+ void factorize(const MatrixType& amat);
+
+ /**
+ * Compute an incomplete LU factorization with dual threshold on the matrix mat
+ * No pivoting is done in this version
+ *
+ **/
+ template<typename MatrixType>
+ IncompleteLUT<Scalar>& compute(const MatrixType& amat)
+ {
+ analyzePattern(amat);
+ factorize(amat);
+ return *this;
+ }
+
+ void setDroptol(const RealScalar& droptol);
+ void setFillfactor(int fillfactor);
+
+ template<typename Rhs, typename Dest>
+ void _solve(const Rhs& b, Dest& x) const
+ {
+ x = m_Pinv * b;
+ x = m_lu.template triangularView<UnitLower>().solve(x);
+ x = m_lu.template triangularView<Upper>().solve(x);
+ x = m_P * x;
+ }
+
+ template<typename Rhs> inline const internal::solve_retval<IncompleteLUT, Rhs>
+ solve(const MatrixBase<Rhs>& b) const
+ {
+ eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
+ eigen_assert(cols()==b.rows()
+ && "IncompleteLUT::solve(): invalid number of rows of the right hand side matrix b");
+ return internal::solve_retval<IncompleteLUT, Rhs>(*this, b.derived());
+ }
+
+protected:
+
+ /** keeps off-diagonal entries; drops diagonal entries */
+ struct keep_diag {
+ inline bool operator() (const Index& row, const Index& col, const Scalar&) const
+ {
+ return row!=col;
+ }
+ };
+
+protected:
+
+ FactorType m_lu;
+ RealScalar m_droptol;
+ int m_fillfactor;
+ bool m_analysisIsOk;
+ bool m_factorizationIsOk;
+ bool m_isInitialized;
+ ComputationInfo m_info;
+ PermutationMatrix<Dynamic,Dynamic,Index> m_P; // Fill-reducing permutation
+ PermutationMatrix<Dynamic,Dynamic,Index> m_Pinv; // Inverse permutation
+};
+
+/**
+ * Set control parameter droptol
+ * \param droptol Drop any element whose magnitude is less than this tolerance
+ **/
+template<typename Scalar>
+void IncompleteLUT<Scalar>::setDroptol(const RealScalar& droptol)
+{
+ this->m_droptol = droptol;
+}
+
+/**
+ * Set control parameter fillfactor
+ * \param fillfactor This is used to compute the number @p fill_in of largest elements to keep on each row.
+ **/
+template<typename Scalar>
+void IncompleteLUT<Scalar>::setFillfactor(int fillfactor)
+{
+ this->m_fillfactor = fillfactor;
+}
+
+template <typename Scalar>
+template<typename _MatrixType>
+void IncompleteLUT<Scalar>::analyzePattern(const _MatrixType& amat)
+{
+ // Compute the Fill-reducing permutation
+ SparseMatrix<Scalar,ColMajor, Index> mat1 = amat;
+ SparseMatrix<Scalar,ColMajor, Index> mat2 = amat.transpose();
+ // Symmetrize the pattern
+ // FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice.
+ // on the other hand for a really non-symmetric pattern, mat2*mat1 should be prefered...
+ SparseMatrix<Scalar,ColMajor, Index> AtA = mat2 + mat1;
+ AtA.prune(keep_diag());
+ internal::minimum_degree_ordering<Scalar, Index>(AtA, m_P); // Then compute the AMD ordering...
+
+ m_Pinv = m_P.inverse(); // ... and the inverse permutation
+
+ m_analysisIsOk = true;
+ m_factorizationIsOk = false;
+ m_isInitialized = false;
+}
+
+template <typename Scalar>
+template<typename _MatrixType>
+void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
+{
+ using std::sqrt;
+ using std::swap;
+ using std::abs;
+
+ eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix");
+ Index n = amat.cols(); // Size of the matrix
+ m_lu.resize(n,n);
+ // Declare Working vectors and variables
+ Vector u(n) ; // real values of the row -- maximum size is n --
+ VectorXi ju(n); // column position of the values in u -- maximum size is n
+ VectorXi jr(n); // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1
+
+ // Apply the fill-reducing permutation
+ eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+ SparseMatrix<Scalar,RowMajor, Index> mat;
+ mat = amat.twistedBy(m_Pinv);
+
+ // Initialization
+ jr.fill(-1);
+ ju.fill(0);
+ u.fill(0);
+
+ // number of largest elements to keep in each row:
+ Index fill_in = static_cast<Index> (amat.nonZeros()*m_fillfactor)/n+1;
+ if (fill_in > n) fill_in = n;
+
+ // number of largest nonzero elements to keep in the L and the U part of the current row:
+ Index nnzL = fill_in/2;
+ Index nnzU = nnzL;
+ m_lu.reserve(n * (nnzL + nnzU + 1));
+
+ // global loop over the rows of the sparse matrix
+ for (Index ii = 0; ii < n; ii++)
+ {
+ // 1 - copy the lower and the upper part of the row i of mat in the working vector u
+
+ Index sizeu = 1; // number of nonzero elements in the upper part of the current row
+ Index sizel = 0; // number of nonzero elements in the lower part of the current row
+ ju(ii) = ii;
+ u(ii) = 0;
+ jr(ii) = ii;
+ RealScalar rownorm = 0;
+
+ typename FactorType::InnerIterator j_it(mat, ii); // Iterate through the current row ii
+ for (; j_it; ++j_it)
+ {
+ Index k = j_it.index();
+ if (k < ii)
+ {
+ // copy the lower part
+ ju(sizel) = k;
+ u(sizel) = j_it.value();
+ jr(k) = sizel;
+ ++sizel;
+ }
+ else if (k == ii)
+ {
+ u(ii) = j_it.value();
+ }
+ else
+ {
+ // copy the upper part
+ Index jpos = ii + sizeu;
+ ju(jpos) = k;
+ u(jpos) = j_it.value();
+ jr(k) = jpos;
+ ++sizeu;
+ }
+ rownorm += numext::abs2(j_it.value());
+ }
+
+ // 2 - detect possible zero row
+ if(rownorm==0)
+ {
+ m_info = NumericalIssue;
+ return;
+ }
+ // Take the 2-norm of the current row as a relative tolerance
+ rownorm = sqrt(rownorm);
+
+ // 3 - eliminate the previous nonzero rows
+ Index jj = 0;
+ Index len = 0;
+ while (jj < sizel)
+ {
+ // In order to eliminate in the correct order,
+ // we must select first the smallest column index among ju(jj:sizel)
+ Index k;
+ Index minrow = ju.segment(jj,sizel-jj).minCoeff(&k); // k is relative to the segment
+ k += jj;
+ if (minrow != ju(jj))
+ {
+ // swap the two locations
+ Index j = ju(jj);
+ swap(ju(jj), ju(k));
+ jr(minrow) = jj; jr(j) = k;
+ swap(u(jj), u(k));
+ }
+ // Reset this location
+ jr(minrow) = -1;
+
+ // Start elimination
+ typename FactorType::InnerIterator ki_it(m_lu, minrow);
+ while (ki_it && ki_it.index() < minrow) ++ki_it;
+ eigen_internal_assert(ki_it && ki_it.col()==minrow);
+ Scalar fact = u(jj) / ki_it.value();
+
+ // drop too small elements
+ if(abs(fact) <= m_droptol)
+ {
+ jj++;
+ continue;
+ }
+
+ // linear combination of the current row ii and the row minrow
+ ++ki_it;
+ for (; ki_it; ++ki_it)
+ {
+ Scalar prod = fact * ki_it.value();
+ Index j = ki_it.index();
+ Index jpos = jr(j);
+ if (jpos == -1) // fill-in element
+ {
+ Index newpos;
+ if (j >= ii) // dealing with the upper part
+ {
+ newpos = ii + sizeu;
+ sizeu++;
+ eigen_internal_assert(sizeu<=n);
+ }
+ else // dealing with the lower part
+ {
+ newpos = sizel;
+ sizel++;
+ eigen_internal_assert(sizel<=ii);
+ }
+ ju(newpos) = j;
+ u(newpos) = -prod;
+ jr(j) = newpos;
+ }
+ else
+ u(jpos) -= prod;
+ }
+ // store the pivot element
+ u(len) = fact;
+ ju(len) = minrow;
+ ++len;
+
+ jj++;
+ } // end of the elimination on the row ii
+
+ // reset the upper part of the pointer jr to zero
+ for(Index k = 0; k <sizeu; k++) jr(ju(ii+k)) = -1;
+
+ // 4 - partially sort and insert the elements in the m_lu matrix
+
+ // sort the L-part of the row
+ sizel = len;
+ len = (std::min)(sizel, nnzL);
+ typename Vector::SegmentReturnType ul(u.segment(0, sizel));
+ typename VectorXi::SegmentReturnType jul(ju.segment(0, sizel));
+ internal::QuickSplit(ul, jul, len);
+
+ // store the largest m_fill elements of the L part
+ m_lu.startVec(ii);
+ for(Index k = 0; k < len; k++)
+ m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
+
+ // store the diagonal element
+ // apply a shifting rule to avoid zero pivots (we are doing an incomplete factorization)
+ if (u(ii) == Scalar(0))
+ u(ii) = sqrt(m_droptol) * rownorm;
+ m_lu.insertBackByOuterInnerUnordered(ii, ii) = u(ii);
+
+ // sort the U-part of the row
+ // apply the dropping rule first
+ len = 0;
+ for(Index k = 1; k < sizeu; k++)
+ {
+ if(abs(u(ii+k)) > m_droptol * rownorm )
+ {
+ ++len;
+ u(ii + len) = u(ii + k);
+ ju(ii + len) = ju(ii + k);
+ }
+ }
+ sizeu = len + 1; // +1 to take into account the diagonal element
+ len = (std::min)(sizeu, nnzU);
+ typename Vector::SegmentReturnType uu(u.segment(ii+1, sizeu-1));
+ typename VectorXi::SegmentReturnType juu(ju.segment(ii+1, sizeu-1));
+ internal::QuickSplit(uu, juu, len);
+
+ // store the largest elements of the U part
+ for(Index k = ii + 1; k < ii + len; k++)
+ m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
+ }
+
+ m_lu.finalize();
+ m_lu.makeCompressed();
+
+ m_factorizationIsOk = true;
+ m_isInitialized = m_factorizationIsOk;
+ m_info = Success;
+}
+
+namespace internal {
+
+template<typename _MatrixType, typename Rhs>
+struct solve_retval<IncompleteLUT<_MatrixType>, Rhs>
+ : solve_retval_base<IncompleteLUT<_MatrixType>, Rhs>
+{
+ typedef IncompleteLUT<_MatrixType> Dec;
+ EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)
+
+ template<typename Dest> void evalTo(Dest& dst) const
+ {
+ dec()._solve(rhs(),dst);
+ }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_INCOMPLETE_LUT_H