Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues.

The teaching and learning of linear algebra have evolved significantly over recent decades, underpinned by diverse approaches ranging from theoretical expositions to dynamic, model-based ...

Students should ideally have taken the course Mathematical Methods (MA100) or equivalent, entailing intermediate-level knowledge of linear algebra, linear independence, eigenvalues and diagonalisation ...

Prom the point of view of numerical linear algebra, the main novelty of the fast randomized iteration schemes described in this article is that they have dramatically reduced operations and storage ...

L. M. Anguas, M. I. Bueno and F. M. Dopico, A comparison of eigenvalue condition numbers for matrix polynomials, Linear Algebra Appl., 564 (2019), 170-200. L. M. Anguas, F. M. Dopico, R. Hollister and ...

This is a 10-week bridge course that prepares students for the fundamentals of linear algebra as they are used in Applied Statistics courses.

This course will contain further algebra and calculus. As with the course MA107, the emphasis will be on applications in economics and finance. Topics covered: Matrix methods in portfolio analysis.

Abstract. This paper presents two kinds of symmetric tridiagonal plus paw form (hereafter TPPF) matrices, which are the combination of tridiagonal matrices and bordered diagonal matrices. In ...