diff options
author | Gael Guennebaud <g.gael@free.fr> | 2011-12-08 23:22:28 +0100 |
---|---|---|
committer | Gael Guennebaud <g.gael@free.fr> | 2011-12-08 23:22:28 +0100 |
commit | 86bb20c43166cdcdd2727e4dd7eef6d28505dc86 (patch) | |
tree | bce2440232074e0065807af2380b5daea8daca74 | |
parent | e36a4c880a50002e0fd7ea38a0154830b8eda0d5 (diff) |
remove dead code
-rw-r--r-- | unsupported/Eigen/SparseExtra | 27 | ||||
-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 | ||||
-rw-r--r-- | unsupported/test/cg.cpp | 100 | ||||
-rw-r--r-- | unsupported/test/sparse_ldlt.cpp | 179 | ||||
-rw-r--r-- | unsupported/test/sparse_llt.cpp | 144 | ||||
-rw-r--r-- | unsupported/test/sparse_lu_legacy.cpp | 128 |
8 files changed, 0 insertions, 1408 deletions
diff --git a/unsupported/Eigen/SparseExtra b/unsupported/Eigen/SparseExtra index c207bc1dc..a09bfb7e5 100644 --- a/unsupported/Eigen/SparseExtra +++ b/unsupported/Eigen/SparseExtra @@ -29,29 +29,6 @@ namespace Eigen { * \endcode */ -struct DefaultBackend {}; - - -// solver flags -enum { - CompleteFactorization = 0x0000, // the default - IncompleteFactorization = 0x0001, - MemoryEfficient = 0x0002, - - // For LLT Cholesky: - SupernodalMultifrontal = 0x0010, - SupernodalLeftLooking = 0x0020, - - // Ordering methods: - NaturalOrdering = 0x0100, // the default - MinimumDegree_AT_PLUS_A = 0x0200, - MinimumDegree_ATA = 0x0300, - ColApproxMinimumDegree = 0x0400, - Metis = 0x0500, - Scotch = 0x0600, - Chaco = 0x0700, - OrderingMask = 0x0f00 -}; #include "../../Eigen/src/misc/Solve.h" #include "../../Eigen/src/misc/SparseSolve.h" @@ -62,10 +39,6 @@ enum { #include "src/SparseExtra/MarketIO.h" -#include "src/SparseExtra/SparseLLT.h" -#include "src/SparseExtra/SparseLDLTLegacy.h" -#include "src/SparseExtra/SparseLU.h" - } // namespace Eigen #include "../../Eigen/src/Core/util/ReenableStupidWarnings.h" 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 diff --git a/unsupported/test/cg.cpp b/unsupported/test/cg.cpp deleted file mode 100644 index ce22b0edb..000000000 --- a/unsupported/test/cg.cpp +++ /dev/null @@ -1,100 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 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/>. - -#include "sparse.h" -#include <Eigen/IterativeSolvers> - -template<typename Scalar,typename Index> void cg(int size) -{ - double density = (std::max)(8./(size*size), 0.01); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType; - - SparseMatrixType m2(size,size); - DenseMatrix refMat2(size,size); - - DenseVector b = DenseVector::Random(size); - DenseVector ref_x(size), x(size); - - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag, 0, 0); -// for(int i=0; i<rows; ++i) -// m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i))); - - SparseMatrixType m3 = m2 * m2.adjoint(), m3_lo(size,size), m3_up(size,size); - DenseMatrix refMat3 = refMat2 * refMat2.adjoint(); - - m3_lo.template selfadjointView<Lower>().rankUpdate(m2,0); - m3_up.template selfadjointView<Upper>().rankUpdate(m2,0); - - ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b); - - x = ConjugateGradient<SparseMatrixType, Lower>().compute(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, full storage, lower"); - - x.setRandom(); - x = ConjugateGradient<SparseMatrixType, Lower>().compute(m3).solveWithGuess(b,x); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solveWithGuess, full storage, lower"); - - x = ConjugateGradient<SparseMatrixType, Upper>().compute(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, full storage, upper, single dense rhs"); - - x = ConjugateGradient<SparseMatrixType, Lower>(m3_lo).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, lower only, single dense rhs"); - - x = ConjugateGradient<SparseMatrixType, Upper>(m3_up).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, upper only, single dense rhs"); - - - - x = ConjugateGradient<SparseMatrixType, Lower, IdentityPreconditioner>().compute(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, full storage, lower"); - - x = ConjugateGradient<SparseMatrixType, Upper, IdentityPreconditioner>().compute(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, full storage, upper, single dense rhs"); - - x = ConjugateGradient<SparseMatrixType, Lower, IdentityPreconditioner>(m3_lo).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, lower only, single dense rhs"); - - x = ConjugateGradient<SparseMatrixType, Upper, IdentityPreconditioner>(m3_up).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, upper only, single dense rhs"); - - ref_x = refMat2.lu().solve(b); - - x = BiCGSTAB<SparseMatrixType, IdentityPreconditioner>(m2).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "BiCGSTAB: solve, I, single dense rhs"); - - x = BiCGSTAB<SparseMatrixType>(m2).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "BiCGSTAB: solve, diag, single dense rhs"); -} - -void test_cg() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( (cg<double,int>(8)) ); - CALL_SUBTEST_1( (cg<double,long int>(8)) ); - CALL_SUBTEST_2( (cg<std::complex<double>,int>(internal::random<int>(1,300))) ); - CALL_SUBTEST_1( (cg<double,int>(internal::random<int>(1,300))) ); - } -} diff --git a/unsupported/test/sparse_ldlt.cpp b/unsupported/test/sparse_ldlt.cpp deleted file mode 100644 index f6426b54e..000000000 --- a/unsupported/test/sparse_ldlt.cpp +++ /dev/null @@ -1,179 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.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/>. - -#define EIGEN_NO_DEPRECATED_WARNING - -#include "sparse.h" -#include <Eigen/SparseExtra> - -#ifdef EIGEN_CHOLMOD_SUPPORT -#include <Eigen/CholmodSupport> -#endif - -template<typename Scalar,typename Index> void sparse_ldlt(int rows, int cols) -{ - static bool odd = true; - odd = !odd; - double density = (std::max)(8./(rows*cols), 0.01); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType; - - SparseMatrixType m2(rows, cols); - DenseMatrix refMat2(rows, cols); - - DenseVector b = DenseVector::Random(cols); - DenseVector refX(cols), x(cols); - - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, 0, 0); - - SparseMatrixType m3 = m2 * m2.adjoint(), m3_lo(rows,rows), m3_up(rows,rows); - DenseMatrix refMat3 = refMat2 * refMat2.adjoint(); - - refX = refMat3.template selfadjointView<Upper>().ldlt().solve(b); - typedef SparseMatrix<Scalar,Upper|SelfAdjoint,Index> SparseSelfAdjointMatrix; - x = b; - SparseLDLT<SparseSelfAdjointMatrix> ldlt(m3); - if (ldlt.succeeded()) - ldlt.solveInPlace(x); - else - std::cerr << "warning LDLT failed\n"; - - VERIFY_IS_APPROX(refMat3.template selfadjointView<Upper>() * x, b); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: default"); - -#ifdef EIGEN_CHOLMOD_SUPPORT - { - x = b; - SparseLDLT<SparseSelfAdjointMatrix, Cholmod> ldlt2(m3); - if (ldlt2.succeeded()) - { - ldlt2.solveInPlace(x); - VERIFY_IS_APPROX(refMat3.template selfadjointView<Upper>() * x, b); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: cholmod solveInPlace"); - - x = ldlt2.solve(b); - VERIFY_IS_APPROX(refMat3.template selfadjointView<Upper>() * x, b); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: cholmod solve"); - } - else - std::cerr << "warning LDLT failed\n"; - } -#endif - - // new Simplicial LLT - - - // new API - { - SparseMatrixType m2(rows, cols); - DenseMatrix refMat2(rows, cols); - - DenseVector b = DenseVector::Random(cols); - DenseVector ref_x(cols), x(cols); - DenseMatrix B = DenseMatrix::Random(rows,cols); - DenseMatrix ref_X(rows,cols), X(rows,cols); - - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, 0, 0); - - for(int i=0; i<rows; ++i) - m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i))); - - - SparseMatrixType m3 = m2 * m2.adjoint(), m3_lo(rows,rows), m3_up(rows,rows); - DenseMatrix refMat3 = refMat2 * refMat2.adjoint(); - - m3_lo.template selfadjointView<Lower>().rankUpdate(m2,0); - m3_up.template selfadjointView<Upper>().rankUpdate(m2,0); - - // with a single vector as the rhs - ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b); - - x = SimplicialCholesky<SparseMatrixType, Lower>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "SimplicialCholesky: solve, full storage, lower, single dense rhs"); - - x = SimplicialCholesky<SparseMatrixType, Upper>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "SimplicialCholesky: solve, full storage, upper, single dense rhs"); - - x = SimplicialCholesky<SparseMatrixType, Lower>(m3_lo).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "SimplicialCholesky: solve, lower only, single dense rhs"); - - x = SimplicialCholesky<SparseMatrixType, Upper>(m3_up).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "SimplicialCholesky: solve, upper only, single dense rhs"); - - - // with multiple rhs - ref_X = refMat3.template selfadjointView<Lower>().llt().solve(B); - - X = SimplicialCholesky<SparseMatrixType, Lower>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(B); - VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "SimplicialCholesky: solve, full storage, lower, multiple dense rhs"); - - X = SimplicialCholesky<SparseMatrixType, Upper>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(B); - VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "SimplicialCholesky: solve, full storage, upper, multiple dense rhs"); - - - // with a sparse rhs - SparseMatrixType spB(rows,cols), spX(rows,cols); - B.diagonal().array() += 1; - spB = B.sparseView(0.5,1); - - ref_X = refMat3.template selfadjointView<Lower>().llt().solve(DenseMatrix(spB)); - - spX = SimplicialCholesky<SparseMatrixType, Lower>(m3).solve(spB); - VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: SimplicialCholesky solve, multiple sparse rhs"); -// - spX = SimplicialCholesky<SparseMatrixType, Upper>(m3).solve(spB); - VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: SimplicialCholesky solve, multiple sparse rhs"); - } - - - -// for(int i=0; i<rows; ++i) -// m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i))); -// -// refX = refMat2.template selfadjointView<Upper>().ldlt().solve(b); -// typedef SparseMatrix<Scalar,Upper|SelfAdjoint> SparseSelfAdjointMatrix; -// x = b; -// SparseLDLT<SparseSelfAdjointMatrix> ldlt(m2); -// if (ldlt.succeeded()) -// ldlt.solveInPlace(x); -// else -// std::cerr << "warning LDLT failed\n"; -// -// VERIFY_IS_APPROX(refMat2.template selfadjointView<Upper>() * x, b); -// VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: default"); - - -} - -void test_sparse_ldlt() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( (sparse_ldlt<double,int>(8, 8)) ); - CALL_SUBTEST_1( (sparse_ldlt<double,long int>(8, 8)) ); - int s = internal::random<int>(1,300); - CALL_SUBTEST_2( (sparse_ldlt<std::complex<double>,int>(s,s)) ); - CALL_SUBTEST_1( (sparse_ldlt<double,int>(s,s)) ); - } -} diff --git a/unsupported/test/sparse_llt.cpp b/unsupported/test/sparse_llt.cpp deleted file mode 100644 index 040681593..000000000 --- a/unsupported/test/sparse_llt.cpp +++ /dev/null @@ -1,144 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.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/>. - -#define EIGEN_NO_DEPRECATED_WARNING - -#include "sparse.h" -#include <Eigen/SparseExtra> - -#ifdef EIGEN_CHOLMOD_SUPPORT -#include <Eigen/CholmodSupport> -#endif - -template<typename Scalar,typename Index> void sparse_llt(int rows, int cols) -{ - double density = (std::max)(8./(rows*cols), 0.01); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType; - - // TODO fix the issue with complex (see SparseLLT::solveInPlace) - SparseMatrixType m2(rows, cols); - DenseMatrix refMat2(rows, cols); - - DenseVector b = DenseVector::Random(cols); - DenseVector ref_x(cols), x(cols); - DenseMatrix B = DenseMatrix::Random(rows,cols); - DenseMatrix ref_X(rows,cols), X(rows,cols); - - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, 0, 0); - - for(int i=0; i<rows; ++i) - m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i))); - - ref_x = refMat2.template selfadjointView<Lower>().llt().solve(b); - if (!NumTraits<Scalar>::IsComplex) - { - x = b; - SparseLLT<SparseMatrixType > (m2).solveInPlace(x); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: default"); - } - -#ifdef EIGEN_CHOLMOD_SUPPORT - // legacy API - { - // Cholmod, as configured in CholmodSupport.h, only supports self-adjoint matrices - SparseMatrixType m3 = m2.adjoint()*m2; - DenseMatrix refMat3 = refMat2.adjoint()*refMat2; - - ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b); - - x = b; - SparseLLT<SparseMatrixType, Cholmod>(m3).solveInPlace(x); - VERIFY((m3*x).isApprox(b,test_precision<Scalar>()) && "LLT legacy: cholmod solveInPlace"); - - x = SparseLLT<SparseMatrixType, Cholmod>(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT legacy: cholmod solve"); - } - - // new API - { - // Cholmod, as configured in CholmodSupport.h, only supports self-adjoint matrices - SparseMatrixType m3 = m2 * m2.adjoint(), m3_lo(rows,rows), m3_up(rows,rows); - DenseMatrix refMat3 = refMat2 * refMat2.adjoint(); - - m3_lo.template selfadjointView<Lower>().rankUpdate(m2,0); - m3_up.template selfadjointView<Upper>().rankUpdate(m2,0); - - // with a single vector as the rhs - ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b); - - x = CholmodDecomposition<SparseMatrixType, Lower>(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs"); - - x = CholmodDecomposition<SparseMatrixType, Upper>(m3).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs"); - - x = CholmodDecomposition<SparseMatrixType, Lower>(m3_lo).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs"); - - x = CholmodDecomposition<SparseMatrixType, Upper>(m3_up).solve(b); - VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs"); - - - // with multiple rhs - ref_X = refMat3.template selfadjointView<Lower>().llt().solve(B); - - #ifndef EIGEN_DEFAULT_TO_ROW_MAJOR - // TODO make sure the API is properly documented about this fact - X = CholmodDecomposition<SparseMatrixType, Lower>(m3).solve(B); - VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "LLT: cholmod solve, multiple dense rhs"); - - X = CholmodDecomposition<SparseMatrixType, Upper>(m3).solve(B); - VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "LLT: cholmod solve, multiple dense rhs"); - #endif - - - // with a sparse rhs - SparseMatrixType spB(rows,cols), spX(rows,cols); - B.diagonal().array() += 1; - spB = B.sparseView(0.5,1); - - ref_X = refMat3.template selfadjointView<Lower>().llt().solve(DenseMatrix(spB)); - - spX = CholmodDecomposition<SparseMatrixType, Lower>(m3).solve(spB); - VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs"); - - spX = CholmodDecomposition<SparseMatrixType, Upper>(m3).solve(spB); - VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs"); - } -#endif - -} - -void test_sparse_llt() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1( (sparse_llt<double,int>(8, 8)) ); - int s = internal::random<int>(1,300); - CALL_SUBTEST_2( (sparse_llt<std::complex<double>,int>(s,s)) ); - CALL_SUBTEST_1( (sparse_llt<double,int>(s,s)) ); - CALL_SUBTEST_1( (sparse_llt<double,long int>(s,s)) ); - } -} diff --git a/unsupported/test/sparse_lu_legacy.cpp b/unsupported/test/sparse_lu_legacy.cpp deleted file mode 100644 index 341e9a6ea..000000000 --- a/unsupported/test/sparse_lu_legacy.cpp +++ /dev/null @@ -1,128 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.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/>. - -#define EIGEN_NO_DEPRECATED_WARNING - -#include "sparse.h" -#include <Eigen/SparseExtra> - -#ifdef EIGEN_UMFPACK_SUPPORT -#include <Eigen/UmfPackSupport> -#endif - -#ifdef EIGEN_SUPERLU_SUPPORT -#include <Eigen/SuperLUSupport> -#endif - - -template<typename Scalar> void sparse_lu_legacy(int rows, int cols) -{ - double density = (std::max)(8./(rows*cols), 0.01); - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - typedef Matrix<Scalar,Dynamic,1> DenseVector; - - DenseVector vec1 = DenseVector::Random(rows); - - std::vector<Vector2i> zeroCoords; - std::vector<Vector2i> nonzeroCoords; - - SparseMatrix<Scalar> m2(rows, cols); - DenseMatrix refMat2(rows, cols); - - DenseVector b = DenseVector::Random(cols); - DenseVector refX(cols), x(cols); - - initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag, &zeroCoords, &nonzeroCoords); - - FullPivLU<DenseMatrix> refLu(refMat2); - refX = refLu.solve(b); - #if defined(EIGEN_SUPERLU_SUPPORT) || defined(EIGEN_UMFPACK_SUPPORT) - Scalar refDet = refLu.determinant(); - #endif - x.setZero(); - // // SparseLU<SparseMatrix<Scalar> > (m2).solve(b,&x); - // // VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: default"); - - #ifdef EIGEN_UMFPACK_SUPPORT - { - // check solve - x.setZero(); - SparseLU<SparseMatrix<Scalar>,UmfPack> lu(m2); - VERIFY(lu.succeeded() && "umfpack LU decomposition failed"); - VERIFY(lu.solve(b,&x) && "umfpack LU solving failed"); - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: umfpack"); - VERIFY_IS_APPROX(refDet,lu.determinant()); - // TODO check the extracted data - //std::cerr << slu.matrixL() << "\n"; - } - #endif - - #ifdef EIGEN_SUPERLU_SUPPORT - // legacy, deprecated API - { - x.setZero(); - SparseLU<SparseMatrix<Scalar>,SuperLULegacy> slu(m2); - if (slu.succeeded()) - { - DenseVector oldb = b; - if (slu.solve(b,&x)) { - VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: SuperLU"); - } - else - std::cerr << "super lu solving failed\n"; - VERIFY(oldb.isApprox(b) && "the rhs should not be modified!"); - - // std::cerr << refDet << " == " << slu.determinant() << "\n"; - if (slu.solve(b, &x, SvTranspose)) { - VERIFY(b.isApprox(m2.transpose() * x, test_precision<Scalar>())); - } - else - std::cerr << "super lu solving failed\n"; - - if (slu.solve(b, &x, SvAdjoint)) { - VERIFY(b.isApprox(m2.adjoint() * x, test_precision<Scalar>())); - } - else - std::cerr << "super lu solving failed\n"; - - if (!NumTraits<Scalar>::IsComplex) { - VERIFY_IS_APPROX(refDet,slu.determinant()); // FIXME det is not very stable for complex - } - } - else - std::cerr << "super lu factorize failed\n"; - } - #endif - -} - -void test_sparse_lu_legacy() -{ - for(int i = 0; i < g_repeat; i++) { - CALL_SUBTEST_1(sparse_lu_legacy<double>(8, 8) ); - int s = internal::random<int>(1,300); - CALL_SUBTEST_1(sparse_lu_legacy<std::complex<double> >(s,s) ); - CALL_SUBTEST_1(sparse_lu_legacy<double>(s,s) ); - } -} |