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move sparse solvers from unsupported/ to main Eigen/ and remove the "not stable yet" warning
This commit is contained in:
Gael Guennebaud
253
Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
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253
Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
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#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
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namespace internal {
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template<typename Lhs, typename Rhs, typename ResultType>
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static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef typename remove_all<Lhs>::type::Scalar Scalar;
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typedef typename remove_all<Lhs>::type::Index Index;
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// make sure to call innerSize/outerSize since we fake the storage order.
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Index rows = lhs.innerSize();
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Index cols = rhs.outerSize();
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eigen_assert(lhs.outerSize() == rhs.innerSize());
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std::vector<bool> mask(rows,false);
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Matrix<Scalar,Dynamic,1> values(rows);
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Matrix<Index,Dynamic,1> indices(rows);
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// estimate the number of non zero entries
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float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
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float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
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float ratioRes = (std::min)(ratioLhs * avgNnzPerRhsColumn, 1.f);
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res.setZero();
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res.reserve(Index(ratioRes*rows*cols));
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// we compute each column of the result, one after the other
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for (Index j=0; j<cols; ++j)
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{
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res.startVec(j);
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Index nnz = 0;
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for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
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{
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Scalar y = rhsIt.value();
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Index k = rhsIt.index();
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for (typename Lhs::InnerIterator lhsIt(lhs, k); lhsIt; ++lhsIt)
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{
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Index i = lhsIt.index();
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Scalar x = lhsIt.value();
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if(!mask[i])
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{
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mask[i] = true;
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values[i] = x * y;
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indices[nnz] = i;
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++nnz;
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}
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else
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values[i] += x * y;
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}
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}
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// unordered insertion
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for(int k=0; k<nnz; ++k)
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{
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int i = indices[k];
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res.insertBackByOuterInnerUnordered(j,i) = values[i];
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mask[i] = false;
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}
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#if 0
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// alternative ordered insertion code:
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int t200 = rows/(log2(200)*1.39);
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int t = (rows*100)/139;
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// FIXME reserve nnz non zeros
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// FIXME implement fast sort algorithms for very small nnz
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// if the result is sparse enough => use a quick sort
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// otherwise => loop through the entire vector
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// In order to avoid to perform an expensive log2 when the
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// result is clearly very sparse we use a linear bound up to 200.
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//if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t)
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//res.startVec(j);
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if(true)
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{
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if(nnz>1) std::sort(indices.data(),indices.data()+nnz);
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for(int k=0; k<nnz; ++k)
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{
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int i = indices[k];
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res.insertBackByOuterInner(j,i) = values[i];
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mask[i] = false;
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}
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}
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else
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{
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// dense path
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for(int i=0; i<rows; ++i)
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{
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if(mask[i])
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{
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mask[i] = false;
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res.insertBackByOuterInner(j,i) = values[i];
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}
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}
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}
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#endif
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}
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res.finalize();
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}
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} // end namespace internal
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namespace internal {
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template<typename Lhs, typename Rhs, typename ResultType,
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int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
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int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
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int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
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struct conservative_sparse_sparse_product_selector;
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template<typename Lhs, typename Rhs, typename ResultType>
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struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
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{
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typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
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typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
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ColMajorMatrix resCol(lhs.rows(),rhs.cols());
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conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
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// sort the non zeros:
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RowMajorMatrix resRow(resCol);
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res = resRow;
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}
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};
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template<typename Lhs, typename Rhs, typename ResultType>
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struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
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RowMajorMatrix rhsRow = rhs;
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RowMajorMatrix resRow(lhs.rows(), rhs.cols());
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conservative_sparse_sparse_product_impl<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);
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res = resRow;
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}
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};
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template<typename Lhs, typename Rhs, typename ResultType>
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struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
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RowMajorMatrix lhsRow = lhs;
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RowMajorMatrix resRow(lhs.rows(), rhs.cols());
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conservative_sparse_sparse_product_impl<Rhs,RowMajorMatrix,RowMajorMatrix>(rhs, lhsRow, resRow);
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res = resRow;
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}
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};
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template<typename Lhs, typename Rhs, typename ResultType>
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struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
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RowMajorMatrix resRow(lhs.rows(), rhs.cols());
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conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
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res = resRow;
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}
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};
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template<typename Lhs, typename Rhs, typename ResultType>
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struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
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{
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typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
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ColMajorMatrix resCol(lhs.rows(), rhs.cols());
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conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
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res = resCol;
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}
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};
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template<typename Lhs, typename Rhs, typename ResultType>
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struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
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ColMajorMatrix lhsCol = lhs;
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ColMajorMatrix resCol(lhs.rows(), rhs.cols());
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conservative_sparse_sparse_product_impl<ColMajorMatrix,Rhs,ColMajorMatrix>(lhsCol, rhs, resCol);
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res = resCol;
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}
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};
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template<typename Lhs, typename Rhs, typename ResultType>
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struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
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ColMajorMatrix rhsCol = rhs;
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ColMajorMatrix resCol(lhs.rows(), rhs.cols());
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conservative_sparse_sparse_product_impl<Lhs,ColMajorMatrix,ColMajorMatrix>(lhs, rhsCol, resCol);
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res = resCol;
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}
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};
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template<typename Lhs, typename Rhs, typename ResultType>
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struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
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typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
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RowMajorMatrix resRow(lhs.rows(),rhs.cols());
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conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
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// sort the non zeros:
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ColMajorMatrix resCol(resRow);
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res = resCol;
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}
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};
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} // end namespace internal
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#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
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