Optimization Theory (MATH 273A), Fall 2025
This is the primary course website for Optimization Theory (MATH 273A), Fall 2025.
Announcements
Homework
There will be several assignments involving both theoretical and computational exercises.
Lecture material
- Chapter 0: Introduction and Preliminaries
- Chapter 1: Gradient Descent
- Chapter 2: Stochastic Gradient Descent
- Chapter 3: ADMM
- Chapter A: Convex analysis
Course Information
Instructor
Ernest K. Ryu, Mathematical Sciences 7619B,
Office hours: Wednesday 11:00–12:30pm
Lectures
Monday, Wednesday, and Friday 10:00–10:50am at Mathematical Sciences 7608.
Exams
This class will have in-person closed-book hand-written (no computers) midterm and final exams.
- Midterm exam: Date and location TBD.
- Final exam: Date and location TBD.
Grading
Homework 30%, midterm exam 30%, final exam 40%.
Course Overview
Introduction to basic optimization theory, recognition of solutions, and geometry of optimization. Some convex analysis, separation hyperplane, and duality theory. Basic optimization algorithms and their rates of convergence.
Prerequisites
Students should have familiarity with undergraduate-level analysis at the level of MATH 131A and probability theory at the level of MATH170A. The assignments will involve programming so an undergraduate course in programming is recommended.
Textbooks
This course will not have a recommended textbook; instead, course notes will be provided.