
# Computational methods in optimization

## Announcements

2020-03-26
Project details posted
2020-03-02
Homework 7 posted
2020-02-20
Homework 6 posted
2020-02-25
Homework 6 posted
2020-02-18
Homework 5 posted
2020-02-10
Homework 4 posted
2020-02-03
Homework 3 posted
2020-01-27
Homework 2 posted
2020-01-14
Homework 1 posted
2020-01-09
Please complete the intro survey by class on 2020-01-16 (submit on Gradescope)

## 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.

The following textbook is required, although please see the notes in the syllabus before purchasing a copy

Algorithms for Optimization Mykel Kochenderfer and Tim A. Wheeler. MIT Press, 2019. Julia Notebooks Errata

The following textbook (available online) will also be used heavily:

Numerical Optimization. Jorge Nocedal and Stephen J. Wright. 2nd edition. Springer, 2006. Available online for Purdue students!
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.