Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of
them. 1 This informal yet practical definition captures the essence of the goal of direct …

The subject of sparse matrices has its root in such diverse fields as management science,
power systems analysis, surveying, circuit theory, and structural analysis. Efficient use of …

The simplex method is frequently the most efficient method of solving linear programming
(LP) problems. This paper reviews previous attempts to parallelise the simplex method in …

Hypergraph partitioning has been an important problem widely encountered in VLSI layout
design [23]. Recent works have introduced new application areas, including one …

Partitioning and load balancing are important problems in scientific computing that can be
modeled as combinatorial problems using graphs or hypergraphs. The Zoltan toolkit was …

Large sparse linear systems of equations are ubiquitous in science, engineering and
beyond. This open access monograph focuses on factorization algorithms for solving such …

K-way hypergraph partitioning has an ever-growing use in parallelization of scientific
computing applications. We claim that hypergraph partitioning with multiple constraints and …

Combinatorial techniques have become essential tools across the landscape of
computational science, and some of the combinatorial ideas undergirding these tools are …

This paper introduces an approach that scales assignment algorithms to large numbers of
robots and tasks. It is especially suitable for dynamic task allocations since both task locality …

In this article, we introduce a cache-oblivious method for sparse matrix–vector multiplication.
Our method attempts to permute the rows and columns of the input matrix using a recursive …