Computational methods in optimization

David Gleich

Purdue University

Spring 2012

Course number CS 59000-OPT

Tuesday and Thursday, 3:00-4:15pm

Lawson B134

Computational methods in optimization

Announcements

2011-04-05

Please read the project description (see right)

2011-02-23

Please complete homework 6 by class on 2012-02-28

2011-02-16

Please complete homework 5 by class on 2012-02-21

2011-02-11

Please complete homework 4 by class on 2012-02-16

2011-02-02

Please complete homework 3 by class on 2012-02-07

2011-01-25

Please complete homework 2 by class on 2012-01-31 (updated 2012-01-26 @ 4:30pm), also re-download the preamble.tex file as it has changed.

2011-01-18

Please complete homework 1 by class on 2012-01-24

2011-01-10

Please complete the intro survey by class on 2012-01-12

Overview

This course is a introduction to optimization for graduate students for those in any computational field.
It will cover many of the fundamentals of optimization and is a good course to prepare those who wish to use optimization in their research and those who wish to become optimizers by developing new algorithms and theory. Selected topics include:

• newton, quasi-newton, and trust region methods for unconstrained problems
• linear programming
• constrained least squares problems
• convex optimization

Prerequisties

We’ll assume you’ve had some background in numerical linear algebra and rely on that subject heavily. Students with a background in mathematical analysis may be able to appreciate some of the more theoretical results as well.

Books and reading materials

The following textbook is required:

Numerical Optimization. Jorge Nocedal and Stephen J. Wright. 2nd edition. Springer, 2006. Available online! doi:10.1007/978-0-387-40065-5

Coursework

This class is a lecture class. Students are expected to attend lectures, and there will be regular homeworks. There will be a midterm exam and a final project.