a logo for the course

Computational Methods in Optimization

David Gleich

Purdue University

Spring 2019

Course number CS-52000

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

Location Forney G124


Computational methods in optimization

Announcements

2019-03-19
Project details posted
2019-02-26
Homework 7 posted
2019-02-20
Homework 6 posted
2019-02-12
Homework 5 posted
2019-02-05
Homework 4 posted
2019-01-29
Homework 3 posted
2019-01-22
Homework 2 posted
2019-01-10
Homework 1 posted
2019-01-08
Please complete the intro survey by class on 2019-01-15 (submit on blackboard)

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:

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.