Fall 2005
CS 383C / CAM 383C / Math 383E
"Numerical Linear Algebra"
TTh 9:30-11am
ETC 2.132
Prof. Inderjit Dhillon
Matrix Computations arise in a varied number of applications, such as,
quantum chemistry computations, statistics, economics, data mining,
etc. This first year graduate course focuses on some of the
fundamental computations that occur in these applications.
The standard problems whose numerical solutions we will study
are (i) systems of linear equations, (ii) least squares problems,
(iii) eigenvalue problems as well as SVD computations. We will also
learn basic principles applicable to a variety of numerical
problems and apply them to the standard problems. These principles
include (i) matrix factorizations, (ii) perturbation theory and
condition numbers, (iii) effects of roundoff error on algorithms
and (iv) analysis of the speed of algorithms.
A substantial portion of this class will be research projects, where
students will have the freedom to choose a well-defined problem of
their choice, or will be assigned a suitable interesting project.
Pre-requisites for this course are a good knowledge of undergraduate
linear algebra, and some mathematical sophistication (students should
have some experience in writing mathematical proofs).
For detailed course information see:
http://www.cs.utexas.edu/users/inderjit/courses/cs383c