Linear algebra - software issues

Testing linear algebra software

How can we test the correctness of a computer implementation of an algorithm such as Gaussian elimination, or the QR algorithm for the eigenproblem? This is an important question for program libraries such as LAPACK, that are designed to run on a wide range of systems. We discuss testing based on verifying known backward or forward error properties of the algorithms, with particular reference t...

Parallel Linear Algebra Software

In this chapter we discuss numerical software for linear algebra problems on parallel computers. We focus on some of the most common numerical operations: linear system solving and eigenvalue computations. Numerical operations such as linear system solving and eigenvalue calculations can be applied to two di erent kinds of matrix: dense and sparse. In dense systems, essentially every matrix ele...

Numerical linear algebra software

at the current iterate x ∈ Rn. In practice, we find that the bulk of computational resources (memory, time) consumed by these algorithms is spent constructing and solving these linear systems, and not in the higher-level algorithmic details. That is not to say that the higher-level details do not impact performance; for example, algorithmic improvements could result in a reduction in the number...

Standardized Numerical Linear Algebra Software

Looking back at dense linear algebra software

Over the years, computational physics and chemistry served as an ongoing source of problems that demanded the ever increasing performance from hardware as well as the software that ran on top of it. Most of these problems could be translated into solutions for systems of linear equations: the very topic of numerical linear algebra. Seemingly then, a set of efficient linear solvers could be solv...