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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 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/>.
+
+#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
+#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType>
+static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+{
+ typedef typename remove_all<Lhs>::type::Scalar Scalar;
+ typedef typename remove_all<Lhs>::type::Index Index;
+
+ // make sure to call innerSize/outerSize since we fake the storage order.
+ Index rows = lhs.innerSize();
+ Index cols = rhs.outerSize();
+ eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+ std::vector<bool> mask(rows,false);
+ Matrix<Scalar,Dynamic,1> values(rows);
+ Matrix<Index,Dynamic,1> indices(rows);
+
+ // estimate the number of non zero entries
+ float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
+ float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
+ float ratioRes = (std::min)(ratioLhs * avgNnzPerRhsColumn, 1.f);
+
+ res.setZero();
+ res.reserve(Index(ratioRes*rows*cols));
+ // we compute each column of the result, one after the other
+ for (Index j=0; j<cols; ++j)
+ {
+
+ res.startVec(j);
+ Index nnz = 0;
+ for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
+ {
+ Scalar y = rhsIt.value();
+ Index k = rhsIt.index();
+ for (typename Lhs::InnerIterator lhsIt(lhs, k); lhsIt; ++lhsIt)
+ {
+ Index i = lhsIt.index();
+ Scalar x = lhsIt.value();
+ if(!mask[i])
+ {
+ mask[i] = true;
+ values[i] = x * y;
+ indices[nnz] = i;
+ ++nnz;
+ }
+ else
+ values[i] += x * y;
+ }
+ }
+
+ // unordered insertion
+ for(int k=0; k<nnz; ++k)
+ {
+ int i = indices[k];
+ res.insertBackByOuterInnerUnordered(j,i) = values[i];
+ mask[i] = false;
+ }
+
+#if 0
+ // alternative ordered insertion code:
+
+ int t200 = rows/(log2(200)*1.39);
+ int t = (rows*100)/139;
+
+ // FIXME reserve nnz non zeros
+ // FIXME implement fast sort algorithms for very small nnz
+ // if the result is sparse enough => use a quick sort
+ // otherwise => loop through the entire vector
+ // In order to avoid to perform an expensive log2 when the
+ // result is clearly very sparse we use a linear bound up to 200.
+ //if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t)
+ //res.startVec(j);
+ if(true)
+ {
+ if(nnz>1) std::sort(indices.data(),indices.data()+nnz);
+ for(int k=0; k<nnz; ++k)
+ {
+ int i = indices[k];
+ res.insertBackByOuterInner(j,i) = values[i];
+ mask[i] = false;
+ }
+ }
+ else
+ {
+ // dense path
+ for(int i=0; i<rows; ++i)
+ {
+ if(mask[i])
+ {
+ mask[i] = false;
+ res.insertBackByOuterInner(j,i) = values[i];
+ }
+ }
+ }
+#endif
+
+ }
+ res.finalize();
+}
+
+
+} // end namespace internal
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType,
+ int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
+ int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
+ int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
+struct conservative_sparse_sparse_product_selector;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
+{
+ typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+
+ static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+ {
+ typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+ typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+ ColMajorMatrix resCol(lhs.rows(),rhs.cols());
+ conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
+ // sort the non zeros:
+ RowMajorMatrix resRow(resCol);
+ res = resRow;
+ }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
+{
+ static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+ {
+ typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+ RowMajorMatrix rhsRow = rhs;
+ RowMajorMatrix resRow(lhs.rows(), rhs.cols());
+ conservative_sparse_sparse_product_impl<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);
+ res = resRow;
+ }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
+{
+ static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+ {
+ typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+ RowMajorMatrix lhsRow = lhs;
+ RowMajorMatrix resRow(lhs.rows(), rhs.cols());
+ conservative_sparse_sparse_product_impl<Rhs,RowMajorMatrix,RowMajorMatrix>(rhs, lhsRow, resRow);
+ res = resRow;
+ }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
+{
+ static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+ {
+ typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+ RowMajorMatrix resRow(lhs.rows(), rhs.cols());
+ conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
+ res = resRow;
+ }
+};
+
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
+{
+ typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
+
+ static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+ {
+ typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+ ColMajorMatrix resCol(lhs.rows(), rhs.cols());
+ conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
+ res = resCol;
+ }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
+{
+ static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+ {
+ typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+ ColMajorMatrix lhsCol = lhs;
+ ColMajorMatrix resCol(lhs.rows(), rhs.cols());
+ conservative_sparse_sparse_product_impl<ColMajorMatrix,Rhs,ColMajorMatrix>(lhsCol, rhs, resCol);
+ res = resCol;
+ }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
+{
+ static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+ {
+ typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+ ColMajorMatrix rhsCol = rhs;
+ ColMajorMatrix resCol(lhs.rows(), rhs.cols());
+ conservative_sparse_sparse_product_impl<Lhs,ColMajorMatrix,ColMajorMatrix>(lhs, rhsCol, resCol);
+ res = resCol;
+ }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
+{
+ static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+ {
+ typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
+ typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
+ RowMajorMatrix resRow(lhs.rows(),rhs.cols());
+ conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
+ // sort the non zeros:
+ ColMajorMatrix resCol(resRow);
+ res = resCol;
+ }
+};
+
+} // end namespace internal
+
+
+#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
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