# CSE 106 - Syllabus

## Syllabus

Least squares method

Chapter 1 of Boyd and Vandenberghe's book

Least squares method (Notes from Auburn)

Convex sets

Chapter 2 of Boyd and Vandenberghe's book

Chapter 3 of Boyd and Vanderberghe

Chapter 4 of Boyd and Vandenberghe

Chapter 5 of Boyd and Vendenberghe

Chapter 10 of Lauritzen

Inequalities and optimization

Optimization without calculus (Chapter 2, sections 2.1-2.8, Byrne)

Fourier-Motzkin elimination (Chapter 1, Lauritzen)

## Practice Problem Sets

## Textbooks and Lecture Notes

**Primary**

Convex Optimization by Stephen Boyd and Lieven Vandenberghe. Book is available at the author's website

Lectures on Convex Optimization by Changho Suh

**Secondary**

Undergraduate convexity, Niels Lauritzen; UCSD Library

A First Course on Optimization, Charles Byrne; PDF file from author's web page,

Algorithms for Convex Optimization, Nisheeth Vishnoi, Cambridge University Press, 2021.

Convex Optimization - Algorithms and Complexity, Sebastien Bubeck; PDF from author's web page

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics, Justin Solomon, A free copy the book is available from Justin Solomon's web page at http://people.csail.mit.edu/jsolomon/

Understanding and using linear programming, Jiri Matousek and Bernd GĂ¤rtner (accessible with UC San Diego credentials)

An Introduction to Optimization, Edwin Chong and Stanislaw Zak, Wiley, 4th edition.

## Supplementary Reading

Linear algebra algorithms

Article on singular value decomposition (Dan Kalman)

MIT notes on singular value decomposition (Gilbert and Strang)

Chapter on singular value decomposition from Lay's book (Lay)