# 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.

### Books and reading materials

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.