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Computational Methods in Optimization

David Gleich

Purdue University

Spring 2017

Course number CS-52000

Tuesday and Thursday, 1:30-2:45pm

Location Lawson B134


Computational methods in optimization

Announcements

2017-03-21
Project details posted
2017-02-27
Homework 7 will be due by Friday 2017-03-03 (submit on Blackboard, per the standard class extension).
2017-02-20
Homework 6 will be due by Friday 2017-02-24 (submit on Blackboard, per the standard class extension).
2017-02-11
Homework 5 will be due by Friday 2017-02-17 (submit on Blackboard, per the standard class extension).
2017-02-05
Homework 4 will be due by Friday 2017-02-10 (submit on Blackboard, per the standard class extension).
2017-01-27
Homework 3 will be due by Friday 2017-02-03 (submit on Blackboard, per the standard class extension).
2017-01-21
Homework 2 will be due by Friday 2017-01-27 (submit on Blackboard, per the standard class extension).
2017-01-12
Homework 1 will be due by Friday 2017-01-20 (submit on Blackboard).
2017-01-09
Please complete the intro survey by class on 2017-01-13 (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.