diff options
author | Gael Guennebaud <g.gael@free.fr> | 2011-12-08 23:22:28 +0100 |
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committer | Gael Guennebaud <g.gael@free.fr> | 2011-12-08 23:22:28 +0100 |
commit | 86bb20c43166cdcdd2727e4dd7eef6d28505dc86 (patch) | |
tree | bce2440232074e0065807af2380b5daea8daca74 /unsupported/Eigen/src/SparseExtra | |
parent | e36a4c880a50002e0fd7ea38a0154830b8eda0d5 (diff) |
remove dead code
Diffstat (limited to 'unsupported/Eigen/src/SparseExtra')
-rw-r--r-- | unsupported/Eigen/src/SparseExtra/SparseLDLTLegacy.h | 416 | ||||
-rw-r--r-- | unsupported/Eigen/src/SparseExtra/SparseLLT.h | 248 | ||||
-rw-r--r-- | unsupported/Eigen/src/SparseExtra/SparseLU.h | 166 |
3 files changed, 0 insertions, 830 deletions
diff --git a/unsupported/Eigen/src/SparseExtra/SparseLDLTLegacy.h b/unsupported/Eigen/src/SparseExtra/SparseLDLTLegacy.h deleted file mode 100644 index e85826acf..000000000 --- a/unsupported/Eigen/src/SparseExtra/SparseLDLTLegacy.h +++ /dev/null @@ -1,416 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// Eigen 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 3 of the License, or (at your option) any later version. -// -// Alternatively, you can redistribute it and/or -// modify it under the terms of the GNU General Public License as -// published by the Free Software Foundation; either version 2 of -// the License, or (at your option) any later version. -// -// Eigen 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 or the -// GNU General Public License for more details. -// -// You should have received a copy of the GNU Lesser General Public -// License and a copy of the GNU General Public License along with -// Eigen. If not, see <http://www.gnu.org/licenses/>. - -/* - -NOTE: the _symbolic, and _numeric functions has 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 - Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 - USA - - Permission is hereby granted to use or copy this program under the - terms of the GNU LGPL, provided that the Copyright, this License, - and the Availability of the original version is retained on all copies. - User documentation of any code that uses this code or any modified - version of this code must cite the Copyright, this License, the - Availability note, and "Used by permission." Permission to modify - the code and to distribute modified code is granted, provided the - Copyright, this License, and the Availability note are retained, - and a notice that the code was modified is included. - */ - -#ifndef EIGEN_SPARSELDLT_LEGACY_H -#define EIGEN_SPARSELDLT_LEGACY_H - -/** \deprecated use class SimplicialLDLT, or class SimplicialLLT, class ConjugateGradient - * \ingroup Sparse_Module - * - * \class SparseLDLT - * - * \brief LDLT Cholesky decomposition of a sparse matrix and associated features - * - * \param MatrixType the type of the matrix of which we are computing the LDLT Cholesky decomposition - * - * \warning the upper triangular part has to be specified. The rest of the matrix is not used. The input matrix must be column major. - * - * \sa class LDLT, class LDLT - */ -template<typename _MatrixType, typename Backend = DefaultBackend> -class SparseLDLT -{ - protected: - typedef typename _MatrixType::Scalar Scalar; - typedef typename NumTraits<typename _MatrixType::Scalar>::Real RealScalar; - - typedef Matrix<Scalar,_MatrixType::ColsAtCompileTime,1> VectorType; - - enum { - SupernodalFactorIsDirty = 0x10000, - MatrixLIsDirty = 0x20000 - }; - - public: - typedef _MatrixType MatrixType; - typedef typename MatrixType::Index Index; - typedef SparseMatrix<Scalar,ColMajor,Index> CholMatrixType; - - /** \deprecated the entire class is deprecated - * Creates a dummy LDLT factorization object with flags \a flags. */ - EIGEN_DEPRECATED SparseLDLT(int flags = 0) - : m_flags(flags), m_status(0) - { - eigen_assert((MatrixType::Flags&RowMajorBit)==0); - m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision(); - } - - /** \deprecated the entire class is deprecated - * Creates a LDLT object and compute the respective factorization of \a matrix using - * flags \a flags. */ - EIGEN_DEPRECATED SparseLDLT(const MatrixType& matrix, int flags = 0) - : m_matrix(matrix.rows(), matrix.cols()), m_flags(flags), m_status(0) - { - eigen_assert((MatrixType::Flags&RowMajorBit)==0); - m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision(); - compute(matrix); - } - - /** Sets the relative threshold value used to prune zero coefficients during the decomposition. - * - * Setting a value greater than zero speeds up computation, and yields to an imcomplete - * factorization with fewer non zero coefficients. Such approximate factors are especially - * useful to initialize an iterative solver. - * - * \warning if precision is greater that zero, the LDLT factorization is not guaranteed to succeed - * even if the matrix is positive definite. - * - * Note that the exact meaning of this parameter might depends on the actual - * backend. Moreover, not all backends support this feature. - * - * \sa precision() */ - void setPrecision(RealScalar v) { m_precision = v; } - - /** \returns the current precision. - * - * \sa setPrecision() */ - RealScalar precision() const { return m_precision; } - - /** Sets the flags. Possible values are: - * - CompleteFactorization - * - IncompleteFactorization - * - MemoryEfficient (hint to use the memory most efficient method offered by the backend) - * - SupernodalMultifrontal (implies a complete factorization if supported by the backend, - * overloads the MemoryEfficient flags) - * - SupernodalLeftLooking (implies a complete factorization if supported by the backend, - * overloads the MemoryEfficient flags) - * - * \sa flags() */ - void settags(int f) { m_flags = f; } - /** \returns the current flags */ - int flags() const { return m_flags; } - - /** Computes/re-computes the LDLT factorization */ - void compute(const MatrixType& matrix); - - /** Perform a symbolic factorization */ - void _symbolic(const MatrixType& matrix); - /** Perform the actual factorization using the previously - * computed symbolic factorization */ - bool _numeric(const MatrixType& matrix); - - /** \returns the lower triangular matrix L */ - inline const CholMatrixType& matrixL(void) const { return m_matrix; } - - /** \returns the coefficients of the diagonal matrix D */ - inline VectorType vectorD(void) const { return m_diag; } - - template<typename Derived> - bool solveInPlace(MatrixBase<Derived> &b) const; - - template<typename Rhs> - inline const internal::solve_retval<SparseLDLT<MatrixType>, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(true && "SparseLDLT is not initialized."); - return internal::solve_retval<SparseLDLT<MatrixType>, Rhs>(*this, b.derived()); - } - - inline Index cols() const { return m_matrix.cols(); } - inline Index rows() const { return m_matrix.rows(); } - - inline const VectorType& diag() const { return m_diag; } - - /** \returns true if the factorization succeeded */ - inline bool succeeded(void) const { return m_succeeded; } - - protected: - CholMatrixType m_matrix; - VectorType m_diag; - VectorXi m_parent; // elimination tree - VectorXi m_nonZerosPerCol; -// VectorXi m_w; // workspace - PermutationMatrix<Dynamic,Dynamic,Index> m_P; - PermutationMatrix<Dynamic,Dynamic,Index> m_Pinv; - RealScalar m_precision; - int m_flags; - mutable int m_status; - bool m_succeeded; -}; - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<SparseLDLT<_MatrixType>, Rhs> - : solve_retval_base<SparseLDLT<_MatrixType>, Rhs> -{ - typedef SparseLDLT<_MatrixType> SpLDLTDecType; - EIGEN_MAKE_SOLVE_HELPERS(SpLDLTDecType,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - //Index size = dec().matrixL().rows(); - eigen_assert(dec().matrixL().rows()==rhs().rows()); - - Rhs b(rhs().rows(), rhs().cols()); - b = rhs(); - - if (dec().matrixL().nonZeros()>0) // otherwise L==I - dec().matrixL().template triangularView<UnitLower>().solveInPlace(b); - - b = b.cwiseQuotient(dec().diag()); - if (dec().matrixL().nonZeros()>0) // otherwise L==I - dec().matrixL().adjoint().template triangularView<UnitUpper>().solveInPlace(b); - - dst = b; - - } - -}; - -} // end namespace internal - -/** Computes / recomputes the LDLT decomposition of matrix \a a - * using the default algorithm. - */ -template<typename _MatrixType, typename Backend> -void SparseLDLT<_MatrixType,Backend>::compute(const _MatrixType& a) -{ - _symbolic(a); - m_succeeded = _numeric(a); -} - -template<typename _MatrixType, typename Backend> -void SparseLDLT<_MatrixType,Backend>::_symbolic(const _MatrixType& a) -{ - assert(a.rows()==a.cols()); - const Index size = a.rows(); - m_matrix.resize(size, size); - m_parent.resize(size); - m_nonZerosPerCol.resize(size); - - ei_declare_aligned_stack_constructed_variable(Index, tags, size, 0); - - const Index* Ap = a.outerIndexPtr(); - const Index* Ai = a.innerIndexPtr(); - Index* Lp = m_matrix.outerIndexPtr(); - - const Index* P = 0; - Index* Pinv = 0; - - if(P) - { - m_P.indices() = Map<const Matrix<Index,Dynamic,1> >(P,size); - m_Pinv = m_P.inverse(); - Pinv = m_Pinv.indices().data(); - } - else - { - m_P.resize(0); - m_Pinv.resize(0); - } - - for (Index 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 */ - Index kk = P ? P[k] : k; /* kth original, or permuted, column */ - Index p2 = Ap[kk+1]; - for (Index p = Ap[kk]; p < p2; ++p) - { - /* A (i,k) is nonzero (original or permuted A) */ - Index i = Pinv ? Pinv[Ai[p]] : Ai[p]; - 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 */ - Lp[0] = 0; - for (Index k = 0; k < size; ++k) - Lp[k+1] = Lp[k] + m_nonZerosPerCol[k]; - - m_matrix.resizeNonZeros(Lp[size]); -} - -template<typename _MatrixType, typename Backend> -bool SparseLDLT<_MatrixType,Backend>::_numeric(const _MatrixType& a) -{ - assert(a.rows()==a.cols()); - const Index size = a.rows(); - assert(m_parent.size()==size); - assert(m_nonZerosPerCol.size()==size); - - const Index* Ap = a.outerIndexPtr(); - const Index* Ai = a.innerIndexPtr(); - const Scalar* Ax = a.valuePtr(); - const Index* Lp = m_matrix.outerIndexPtr(); - Index* Li = m_matrix.innerIndexPtr(); - Scalar* Lx = m_matrix.valuePtr(); - m_diag.resize(size); - - ei_declare_aligned_stack_constructed_variable(Scalar, y, size, 0); - ei_declare_aligned_stack_constructed_variable(Index, pattern, size, 0); - ei_declare_aligned_stack_constructed_variable(Index, tags, size, 0); - - Index* P = 0; - Index* Pinv = 0; - - if(m_P.size()==size) - { - P = m_P.indices().data(); - Pinv = m_Pinv.indices().data(); - } - - bool ok = true; - - for (Index 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 */ - Index 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 */ - Index kk = (P) ? (P[k]) : (k); /* kth original, or permuted, column */ - Index p2 = Ap[kk+1]; - for (Index p = Ap[kk]; p < p2; ++p) - { - Index i = Pinv ? Pinv[Ai[p]] : Ai[p]; /* get A(i,k) */ - if (i <= k) - { - y[i] += internal::conj(Ax[p]); /* 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) */ - m_diag[k] = y[k]; /* get D(k,k) 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; - Index p2 = Lp[i] + m_nonZerosPerCol[i]; - Index p; - for (p = Lp[i]; p < p2; ++p) - y[Li[p]] -= internal::conj(Lx[p]) * (yi); - Scalar l_ki = yi / m_diag[i]; /* the nonzero entry L(k,i) */ - m_diag[k] -= l_ki * internal::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 (m_diag[k] == 0.0) - { - ok = false; /* failure, D(k,k) is zero */ - break; - } - } - - return ok; /* success, diagonal of D is all nonzero */ -} - -/** Computes b = L^-T D^-1 L^-1 b */ -template<typename _MatrixType, typename Backend> -template<typename Derived> -bool SparseLDLT<_MatrixType, Backend>::solveInPlace(MatrixBase<Derived> &b) const -{ - //Index size = m_matrix.rows(); - eigen_assert(m_matrix.rows()==b.rows()); - if (!m_succeeded) - return false; - - if(m_P.size()>0) - b = m_Pinv * b; - - if (m_matrix.nonZeros()>0) // otherwise L==I - m_matrix.template triangularView<UnitLower>().solveInPlace(b); - b = b.cwiseQuotient(m_diag); - if (m_matrix.nonZeros()>0) // otherwise L==I - m_matrix.adjoint().template triangularView<UnitUpper>().solveInPlace(b); - - if(m_P.size()>0) - b = m_P * b; - - return true; -} - -#endif // EIGEN_SPARSELDLT_LEGACY_H diff --git a/unsupported/Eigen/src/SparseExtra/SparseLLT.h b/unsupported/Eigen/src/SparseExtra/SparseLLT.h deleted file mode 100644 index fbfeecc35..000000000 --- a/unsupported/Eigen/src/SparseExtra/SparseLLT.h +++ /dev/null @@ -1,248 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// Eigen 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 3 of the License, or (at your option) any later version. -// -// Alternatively, you can redistribute it and/or -// modify it under the terms of the GNU General Public License as -// published by the Free Software Foundation; either version 2 of -// the License, or (at your option) any later version. -// -// Eigen 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 or the -// GNU General Public License for more details. -// -// You should have received a copy of the GNU Lesser General Public -// License and a copy of the GNU General Public License along with -// Eigen. If not, see <http://www.gnu.org/licenses/>. - -#ifndef EIGEN_SPARSELLT_H -#define EIGEN_SPARSELLT_H - -/** \deprecated use class SimplicialLDLT, or class SimplicialLLT, class ConjugateGradient - * \ingroup Sparse_Module - * - * \class SparseLLT - * - * \brief LLT Cholesky decomposition of a sparse matrix and associated features - * - * \param MatrixType the type of the matrix of which we are computing the LLT Cholesky decomposition - * - * \sa class LLT, class LDLT - */ -template<typename _MatrixType, typename Backend = DefaultBackend> -class SparseLLT -{ - protected: - typedef typename _MatrixType::Scalar Scalar; - typedef typename NumTraits<typename _MatrixType::Scalar>::Real RealScalar; - - enum { - SupernodalFactorIsDirty = 0x10000, - MatrixLIsDirty = 0x20000 - }; - - public: - typedef SparseMatrix<Scalar> CholMatrixType; - typedef _MatrixType MatrixType; - typedef typename MatrixType::Index Index; - - /** \deprecated the entire class is deprecated - * Creates a dummy LLT factorization object with flags \a flags. */ - EIGEN_DEPRECATED SparseLLT(int flags = 0) - : m_flags(flags), m_status(0) - { - m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision(); - } - - /** \deprecated the entire class is deprecated - * Creates a LLT object and compute the respective factorization of \a matrix using - * flags \a flags. */ - EIGEN_DEPRECATED SparseLLT(const MatrixType& matrix, int flags = 0) - : m_matrix(matrix.rows(), matrix.cols()), m_flags(flags), m_status(0) - { - m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision(); - compute(matrix); - } - - /** Sets the relative threshold value used to prune zero coefficients during the decomposition. - * - * Setting a value greater than zero speeds up computation, and yields to an imcomplete - * factorization with fewer non zero coefficients. Such approximate factors are especially - * useful to initialize an iterative solver. - * - * \warning if precision is greater that zero, the LLT factorization is not guaranteed to succeed - * even if the matrix is positive definite. - * - * Note that the exact meaning of this parameter might depends on the actual - * backend. Moreover, not all backends support this feature. - * - * \sa precision() */ - void setPrecision(RealScalar v) { m_precision = v; } - - /** \returns the current precision. - * - * \sa setPrecision() */ - RealScalar precision() const { return m_precision; } - - /** Sets the flags. Possible values are: - * - CompleteFactorization - * - IncompleteFactorization - * - MemoryEfficient (hint to use the memory most efficient method offered by the backend) - * - SupernodalMultifrontal (implies a complete factorization if supported by the backend, - * overloads the MemoryEfficient flags) - * - SupernodalLeftLooking (implies a complete factorization if supported by the backend, - * overloads the MemoryEfficient flags) - * - * \sa flags() */ - void setFlags(int f) { m_flags = f; } - /** \returns the current flags */ - int flags() const { return m_flags; } - - /** Computes/re-computes the LLT factorization */ - void compute(const MatrixType& matrix); - - /** \returns the lower triangular matrix L */ - inline const CholMatrixType& matrixL(void) const { return m_matrix; } - - template<typename Derived> - bool solveInPlace(MatrixBase<Derived> &b) const; - - template<typename Rhs> - inline const internal::solve_retval<SparseLLT<MatrixType>, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(true && "SparseLLT is not initialized."); - return internal::solve_retval<SparseLLT<MatrixType>, Rhs>(*this, b.derived()); - } - - inline Index cols() const { return m_matrix.cols(); } - inline Index rows() const { return m_matrix.rows(); } - - /** \returns true if the factorization succeeded */ - inline bool succeeded(void) const { return m_succeeded; } - - protected: - CholMatrixType m_matrix; - RealScalar m_precision; - int m_flags; - mutable int m_status; - bool m_succeeded; -}; - - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<SparseLLT<_MatrixType>, Rhs> - : solve_retval_base<SparseLLT<_MatrixType>, Rhs> -{ - typedef SparseLLT<_MatrixType> SpLLTDecType; - EIGEN_MAKE_SOLVE_HELPERS(SpLLTDecType,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - const Index size = dec().matrixL().rows(); - eigen_assert(size==rhs().rows()); - - Rhs b(rhs().rows(), rhs().cols()); - b = rhs(); - - dec().matrixL().template triangularView<Lower>().solveInPlace(b); - dec().matrixL().adjoint().template triangularView<Upper>().solveInPlace(b); - - dst = b; - - } - -}; - -} // end namespace internal - - -/** Computes / recomputes the LLT decomposition of matrix \a a - * using the default algorithm. - */ -template<typename _MatrixType, typename Backend> -void SparseLLT<_MatrixType,Backend>::compute(const _MatrixType& a) -{ - assert(a.rows()==a.cols()); - const Index size = a.rows(); - m_matrix.resize(size, size); - - // allocate a temporary vector for accumulations - internal::AmbiVector<Scalar,Index> tempVector(size); - RealScalar density = a.nonZeros()/RealScalar(size*size); - - // TODO estimate the number of non zeros - m_matrix.setZero(); - m_matrix.reserve(a.nonZeros()*10); - for (Index j = 0; j < size; ++j) - { - Scalar x = internal::real(a.coeff(j,j)); - - // TODO better estimate of the density ! - tempVector.init(density>0.001? IsDense : IsSparse); - tempVector.setBounds(j+1,size); - tempVector.setZero(); - // init with current matrix a - { - typename _MatrixType::InnerIterator it(a,j); - eigen_assert(it.index()==j && - "matrix must has non zero diagonal entries and only the lower triangular part must be stored"); - ++it; // skip diagonal element - for (; it; ++it) - tempVector.coeffRef(it.index()) = it.value(); - } - for (Index k=0; k<j+1; ++k) - { - typename CholMatrixType::InnerIterator it(m_matrix, k); - while (it && it.index()<j) - ++it; - if (it && it.index()==j) - { - Scalar y = it.value(); - x -= internal::abs2(y); - ++it; // skip j-th element, and process remaining column coefficients - tempVector.restart(); - for (; it; ++it) - { - tempVector.coeffRef(it.index()) -= it.value() * y; - } - } - } - // copy the temporary vector to the respective m_matrix.col() - // while scaling the result by 1/real(x) - RealScalar rx = internal::sqrt(internal::real(x)); - m_matrix.insert(j,j) = rx; // FIXME use insertBack - Scalar y = Scalar(1)/rx; - for (typename internal::AmbiVector<Scalar,Index>::Iterator it(tempVector, m_precision*rx); it; ++it) - { - // FIXME use insertBack - m_matrix.insertBack(it.index(), j) = it.value() * y; - } - } - m_matrix.finalize(); -} - -/** Computes b = L^-T L^-1 b */ -template<typename _MatrixType, typename Backend> -template<typename Derived> -bool SparseLLT<_MatrixType, Backend>::solveInPlace(MatrixBase<Derived> &b) const -{ - const Index size = m_matrix.rows(); - eigen_assert(size==b.rows()); - - m_matrix.template triangularView<Lower>().solveInPlace(b); - m_matrix.adjoint().template triangularView<Upper>().solveInPlace(b); - - return true; -} - -#endif // EIGEN_SPARSELLT_H diff --git a/unsupported/Eigen/src/SparseExtra/SparseLU.h b/unsupported/Eigen/src/SparseExtra/SparseLU.h deleted file mode 100644 index ffcdb88e2..000000000 --- a/unsupported/Eigen/src/SparseExtra/SparseLU.h +++ /dev/null @@ -1,166 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// Eigen 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 3 of the License, or (at your option) any later version. -// -// Alternatively, you can redistribute it and/or -// modify it under the terms of the GNU General Public License as -// published by the Free Software Foundation; either version 2 of -// the License, or (at your option) any later version. -// -// Eigen 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 or the -// GNU General Public License for more details. -// -// You should have received a copy of the GNU Lesser General Public -// License and a copy of the GNU General Public License along with -// Eigen. If not, see <http://www.gnu.org/licenses/>. - -#ifndef EIGEN_SPARSELU_H -#define EIGEN_SPARSELU_H - -enum { - SvNoTrans = 0, - SvTranspose = 1, - SvAdjoint = 2 -}; - -/** \deprecated use class BiCGSTAB, class SuperLU, or class UmfPackLU - * \ingroup Sparse_Module - * - * \class SparseLU - * - * \brief LU decomposition of a sparse matrix and associated features - * - * \param _MatrixType the type of the matrix of which we are computing the LU factorization - * - * \sa class FullPivLU, class SparseLLT - */ -template<typename _MatrixType, typename Backend = DefaultBackend> -class SparseLU - { - protected: - typedef typename _MatrixType::Scalar Scalar; - typedef typename NumTraits<typename _MatrixType::Scalar>::Real RealScalar; - typedef SparseMatrix<Scalar> LUMatrixType; - - enum { - MatrixLUIsDirty = 0x10000 - }; - - public: - typedef _MatrixType MatrixType; - - /** \deprecated the entire class is deprecated - * Creates a dummy LU factorization object with flags \a flags. */ - EIGEN_DEPRECATED SparseLU(int flags = 0) - : m_flags(flags), m_status(0) - { - m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision(); - } - - /** \deprecated the entire class is deprecated - * Creates a LU object and compute the respective factorization of \a matrix using - * flags \a flags. */ - EIGEN_DEPRECATED SparseLU(const _MatrixType& matrix, int flags = 0) - : /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0) - { - m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision(); - compute(matrix); - } - - /** Sets the relative threshold value used to prune zero coefficients during the decomposition. - * - * Setting a value greater than zero speeds up computation, and yields to an imcomplete - * factorization with fewer non zero coefficients. Such approximate factors are especially - * useful to initialize an iterative solver. - * - * Note that the exact meaning of this parameter might depends on the actual - * backend. Moreover, not all backends support this feature. - * - * \sa precision() */ - void setPrecision(RealScalar v) { m_precision = v; } - - /** \returns the current precision. - * - * \sa setPrecision() */ - RealScalar precision() const { return m_precision; } - - /** Sets the flags. Possible values are: - * - CompleteFactorization - * - IncompleteFactorization - * - MemoryEfficient - * - one of the ordering methods - * - etc... - * - * \sa flags() */ - void setFlags(int f) { m_flags = f; } - /** \returns the current flags */ - int flags() const { return m_flags; } - - void setOrderingMethod(int m) - { - eigen_assert( (m&~OrderingMask) == 0 && m!=0 && "invalid ordering method"); - m_flags = m_flags&~OrderingMask | m&OrderingMask; - } - - int orderingMethod() const - { - return m_flags&OrderingMask; - } - - /** Computes/re-computes the LU factorization */ - void compute(const _MatrixType& matrix); - - /** \returns the lower triangular matrix L */ - //inline const _MatrixType& matrixL() const { return m_matrixL; } - - /** \returns the upper triangular matrix U */ - //inline const _MatrixType& matrixU() const { return m_matrixU; } - - template<typename BDerived, typename XDerived> - bool solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x, - const int transposed = SvNoTrans) const; - - /** \returns true if the factorization succeeded */ - inline bool succeeded(void) const { return m_succeeded; } - - protected: - RealScalar m_precision; - int m_flags; - mutable int m_status; - bool m_succeeded; -}; - -/** Computes / recomputes the LU decomposition of matrix \a a - * using the default algorithm. - */ -template<typename _MatrixType, typename Backend> -void SparseLU<_MatrixType,Backend>::compute(const _MatrixType& ) -{ - eigen_assert(false && "not implemented yet"); -} - -/** Computes *x = U^-1 L^-1 b - * - * If \a transpose is set to SvTranspose or SvAdjoint, the solution - * of the transposed/adjoint system is computed instead. - * - * Not all backends implement the solution of the transposed or - * adjoint system. - */ -template<typename _MatrixType, typename Backend> -template<typename BDerived, typename XDerived> -bool SparseLU<_MatrixType,Backend>::solve(const MatrixBase<BDerived> &, MatrixBase<XDerived>* , const int ) const -{ - eigen_assert(false && "not implemented yet"); - return false; -} - -#endif // EIGEN_SPARSELU_H |