Content: Course will cover fundamental parts of numerical linear algebra including matrix factorizations, solution of linear systems and least-squares problems, and the calculation of eigenvalues and eigenvectors. Issues pertaining to conditioning and numerical stability will be thoroughly analyzed. We will also point out and use links to other mathematical and computer science disciplines such as mathematical modelling, computer architectures and parallel computing. Particulars: Excellent background in linear algebra is assumed. Some knowledge of computer architectures, programming and elementary numerical analysis is highly desirable.

Textbook and other reading material:

A. Bjorck, **Numerical Methods in Matrix Computations**.
Texts in Applied Mathematics 59, Springer, 2015.

Also recommended:

G. H. Golub and C. F. Van Loan, **Matrix Computations, 4th Edition**.
Johns Hopkins University Press, 2013.

**Getting started with Matlab.**
This is a brief tutorial written by
** Prof. James Nagy.** It is part of his
textbook with Ian Gladwell and Warren Ferguson, which you can buy
**here**.

Last updated August 10, 2017.