|  | // This file is part of Eigen, a lightweight C++ template library | 
|  | // for linear algebra. | 
|  | // | 
|  | // Copyright (C) 2008-2012 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/. | 
|  |  | 
|  | /* | 
|  | NOTE: these functions have been adapted from the LDL library: | 
|  |  | 
|  | LDL Copyright (c) 2005 by Timothy A. Davis.  All Rights Reserved. | 
|  |  | 
|  | The author of LDL, Timothy A. Davis., has executed a license with Google LLC | 
|  | to permit distribution of this code and derivative works as part of Eigen under | 
|  | the Mozilla Public License v. 2.0, as stated at the top of this file. | 
|  | */ | 
|  |  | 
|  | #ifndef EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H | 
|  | #define EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H | 
|  |  | 
|  | // IWYU pragma: private | 
|  | #include "./InternalHeaderCheck.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, bool NonHermitian> | 
|  | 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] = Scalar(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] += getSymm(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) */ | 
|  |  | 
|  | DiagonalScalar d = | 
|  | getDiag(y[k]) * m_shiftScale + m_shiftOffset;  // get D(k,k), apply the shift function, and clear Y(k) | 
|  | y[k] = Scalar(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] = Scalar(0); | 
|  |  | 
|  | /* the nonzero entry L(k,i) */ | 
|  | Scalar l_ki; | 
|  | if (DoLDLT) | 
|  | l_ki = yi / getDiag(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]] -= getSymm(Lx[p]) * yi; | 
|  | d -= getDiag(l_ki * getSymm(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 (NonHermitian ? d == RealScalar(0) : numext::real(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 |