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implement a more optimistic heuristic to predict the nnz of a saprse*sparse product

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Gael Guennebaud
2011-12-16 15:59:44 +01:00
parent 40c0f3af57
commit 732a50d043
2 changed files with 15 additions and 11 deletions
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11
Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
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@@ -43,12 +43,15 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
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);
// given a rhs column containing Y non zeros, we assume that the respective Y columns
// of the lhs differs in average of one non zeros, thus the number of non zeros for
// the product of a rhs column with the lhs is X+Y where X is the average number of non zero
// per column of the lhs.
// Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
Index estimated_nnz_prod = lhs.nonZeros() + rhs.nonZeros();
res.setZero();
res.reserve(Index(ratioRes*rows*cols));
res.reserve(Index(estimated_nnz_prod));
// we compute each column of the result, one after the other
for (Index j=0; j<cols; ++j)
{