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 …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

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 …

This book deals with numerical methods for solving large sparse linear systems of
equations, particularly those arising from the discretization of partial differential equations. It …

" Models are often the only way of interpreting measurements to in vestigate long-range
transport, and this is the reason for the emphasis on them in many research programs". BEA …

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 second edition of the highly acclaimed and successful first edition, deals primarily with
the analysis of structural engineering systems, with applicable methods to other types of …

'Et moi...-si j'avait su comment en revenir, One service mathematics has rendered the je n'y
serais point aile.'human race. It has put common sense back where it belongs, on the …

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

We review some recent developments in frontal and multifrontal schemes for solving sparse
linear systems, including variants that exploit parallelism and matrix structure. An important …