
# 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:

• 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.