Large-Scale Convex Optimization: Algorithms and Analyses via Monotone Operators, EE 392F (Stanford), Spring 2023

This is the course website for the Stanford University course Large-Scale Convex Optimization: Algorithms and Analyses via Monotone Operators (EE 392F), Spring 2022. We will host the discussion forum in Ed.

Announcements

  • The Ed page has been set up!
  • My office hours have been set to Wednesday 12:00–1:20pm.

Lecture material

Homework

Weekly homework assignments, due each Friday at midnight, starting the second week. Late homework will not be accepted. You are allowed, even encouraged, to work on the homework in small groups, but you must write up your own homework to hand in. Each question on the homework will be graded on a scale of {0, 1, 2}.

  1. Homework 1, Due 04/14, 5pm. 1.1, 1.4, 1.5, 1.7, 1.9, 2.1 of Ryu & Yin.
  2. Homework 2, Due 04/21, 5pm. 2.3, 2.4, 2.9, 2.13, 2.14, 2.15 of Ryu & Yin.
  3. Homework 3, Due 04/28, 5pm. 2.16, 2.17, 2.23, 2.25, 2.34 of Ryu & Yin.
  4. Homework 4, Due 05/05, 5pm. 2.21, 2.36, 3.1, 3.2 of Ryu & Yin.
  5. Homework 5, Due 05/19, 5pm. 9.4, 9.5, 9.6 of Ryu & Yin.
  6. Homework 6, Due 06/02, 5pm. 10.1, 13.1, 13.5, 13.6, 13.11, 13.20 of Ryu & Yin.
  7. Homework 7, Due 06/06, 5pm. 10.5, 10.6, 10.7 of Ryu & Yin.

Lecture Plans and Reading

  • [Week 1-2] Monotone operators
  • [Week 3-4] Primal-dual methods
  • [Week 5] Scaled relative graphs
  • [Week 6-7] Distributed and decentralized optimization
  • [Week 8] Maximality, duality
  • [Week 9-10] ADMM-type methods

Course Information

Instructor

Ernest K. Ryu, Packard 212,
Office hours: Wednesday 12:00–1:20pm

Photo of Ernest Ryu

Lectures

Tuesday and Thursday 12:00–1:20pm at Hewlett Teaching Center Rm 101.

Exams

This class will have in-person (non-take-home) hand-written (no computers) midterm and final exams.

  • Midterm exam: Take-home, May 12th 5:00pm – 13th 5:00pm.
  • Final exam: Take-home, June 9th 5:00pm – 10th 5:00pm.

Grading

Homework 30%, midterm exam 30%, final exam 40%.

Prerequisites

EE 364a. Background in (mathematical) analysis is helpful but not necessary.

Textbooks

We will use Large-Scale Convex Optimization: Algorithms and Analyses via Monotone Operators by Ryu (myself) and Yin. You will have access to a free (legal) electronic copy of the book.