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 …

Since the release of version 2.0 of the LAPACK software and the second edition of the
Users' Guide in 1994, LAPACK has been expanded further and has become an even wider …

A comprehensive treatment of numerical linear algebra from the standpoint of both theory
and practice. The fourth edition of Gene H. Golub and Charles F. Van Loan's classic is an …

In the nearly seven years since I finished writing the first edition of this book research on the
accuracy and stability of numerical algorithms has continued to flourish and mature. Our …

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics-a
very active area of research in which few up-to-date reference works are available. This is …

Large linear systems of saddle point type arise in a wide variety of applications throughout
computational science and engineering. Due to their indefiniteness and often poor spectral …

We survey the quadratic eigenvalue problem, treating its many applications, its
mathematical properties, and a variety of numerical solution techniques. Emphasis is given …

In this review paper, we consider some important developments and trends in algorithm
design for the solution of linear systems concentrating on aspects that involve the …

In many large scale scientific or engineering computations, ranging from computing the
frequency response of a circuit to the earthquake response of a buildingto the energy levels …

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 …