# Computational methods in optimization

## Announcements

- 2023-03-22
- Project details posted
- 2023-02-27
- Homework 7 posted
- 2023-02-20
- Homework 6 posted
- 2023-02-13
- Homework 5 posted
- 2023-02-06
- Homework 4 posted
- 2023-01-30
- Homework 3 posted
- 2023-01-23
- Homework 2 posted
- 2023-01-16
- Homework 1 posted
- 2023-01-06
- Welcome to class, please complete the intro survey
by class on 2023-01-13
(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

I will attempt to add a little more ML-based coverage this year, although
since that is very common in ML-classes, this class will remain focused
on more classic optimization techniques and spend a while on linear
programming and how it works, along with quadratic programming.

## 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.

If you have not taken CS515, or had similar material elsewhere,
you are going to be challenged by this class.

### 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.