a logo for the course

Matrix computations &
Numerical linear algebra

David Gleich

Purdue University

Fall 2012

Course number CS-51500

Tuesday and Thursday, 10:30-11:45am

Location Lawson 1106

Matrix computations & Numerical linear algebra

Announcements

2012-11-27
Homework 7 posted. Due 12/2 at 11:59pm.
2012-11-25
Extra credit posted. Due 12/7 at 5pm.
2012-11-12
Homework 6 posted. Due 11/20 at 5pm.
2012-10-26
Homework 5 posted. Due 11/9 at 5pm.
2012-10-16
Homework 4 posted. Due 10/25 in class.
2012-09-22
Homework 3 posted. Due 9/27 in class.
2012-09-11
Homework 2 posted. Due 9/20 in class.
2012-08-28
Homework 1 posted. Due 9/6 in class.
2012-08-21
Make sure you complete the initial course survey by the 2nd class.
2012-08-20
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 textbook is recommended, but will probably not be required.

Matrix Computations. Gene H. Golub and Charles van Loan. 3rd Edition, Johns Hopkins University Press.

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.

Jianlin Xia is teaching a structured matrix computations class on Tuesday and Thursday afternoons at 1:30am. He will focus specifically on sparse and structured matrices.