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Matrix Computations

David Gleich

Purdue University

Fall 2020

Course number CS-51500

Online due to COVID-19 Pandemic

Social distance, wear a mask.

Lectures posted to Brightspace Tuesday.

Synchronous Zoom meetings Tuesday 10:30-11:30.

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

Location Forney B124

Matrix Computations


Homework 6 posted.
Homework 5 posted.
Homework 4 posted.
Homework 3 posted.
Homework 2 posted.
Homework 1 posted.
Please complete the intro survey and submit on Brightspace.
Make sure to read the syllabus
Welcome! Please browse around the website.


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


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.