Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear
algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations …

In this paper we consider the construction, analysis, implementation and application of
exponential integrators. The focus will be on two types of stiff problems. The first one is …

The massive parallelism of graphics processing units (GPUs) offers tremendous
performance in many high-performance computing applications. While dense linear algebra …

Expokit provides a set of routines aimed at computing matrix exponentials. More precisely, it
computes either a small matrix exponential in full, the action of a large sparse matrix …

The purpose of this book is to unify and document in one place many of the techniques and
much of the current understanding about solving systems of linear equations on vector and …

We describe a repository of data for the testing of numerical algorithms and mathematical
software for matrix computations. The repository is designed to accommodate both dense …

It is shown that the least squares approach to reconciling hierarchical time series forecasts
can be extended to much more general collections of time series with aggregation …

We discuss the organization of frontal matrices in multifrontal methods for the solution of
large sparse sets of unsymmetric linear equations. In the multifrontal method, work on a …

This work presents a systematic exploration on the promise and special challenges of deep
learning for sparse matrix format selection---a problem of determining the best storage …

An immersed boundary method for the incompressible Navier–Stokes equations in irregular
domains is developed using a local ghost cell approach. This method extends the solution …