# Matrix Computations

## Annoucements

- 2018-12-04
- Extra Credit posted.
- 2018-11-27
- Homework 11 posted.
- 2018-11-13
- Homework 10 posted.
- 2018-11-06
- Homework 9 posted.
- 2018-10-29
- Homework 8 posted.
- 2018-10-22
- Homework 7 posted.
- 2018-10-15
- Homework 6 posted.
- 2018-09-24
- Homework 5 posted.
- 2018-09-17
- Homework 4 posted.
- 2018-09-10
- Homework 3 posted.
- 2018-09-03
- Homework 2 posted.
- 2018-08-28
- Homework 1 posted.
- 2018-08-21
- Please complete the intro survey and submit on blackboard.
- 2018-08-17
- Welcome! Please browse around the website.

## Overview

Here's what the Registrar says:

Direct and iterative solvers of dense and
sparse linear systems of equations,
numerical schemes for handling symmetric
algebraic eigenvalue problems, and the
singular-value decomposition and its
applications in linear least squares
problems. Typically offered Spring.

Obviously, this course is being held in the fall, not the spring.
We will mainly focus on dense and sparse linear systems. This will
include a study of conditioning and error analysis in the dense
case, and convergence in the sparse case. We will study on the other
topics as well, but they will receive comparatively less treatment.
I will try and highlight recent research and developments when
it is relevant to the current lectures.

### Books and reading materials

The following two textbooks are highly recommended. They are officially
required, but you won't need them for the statement of any homework
problems.

Matrix Computations. Gene H. Golub and Charles van Loan. 4th Edition,
Johns Hopkins University Press.

Numerical Linear Algebra. Lloyd N. Trefethen and David Bau. SIAM

See the readings page for additional reference

### Coursework

This is a lecture class. You (the student) will be expected to attend
lectures and there will be regular homeworks. There will also be a
midterm and a final.