EE364a: Course Information

Lectures

Our current plan is to have both asynchronous lectures pre-recorded as well as more Socratic interactive sessions via Zoom, where Professor Duchi will work through examples and solve problems.

Office hours

  • John Duchi: As the main lecture time is an exercise session, office hours are by appointment.

TA office hours The TAs will offer informal working sessions, which will also serve as their office hours, beginning the second week of class. Attendance is optional.

  • Karan Chadha: Tuesdays 12–2pm

  • Jongho Kim: Wednesdays 4–6pm

  • Ananya Kumar: Thursdays 1pm–3pm

  • Daniel Levy: Fridays 3pm–5pm.

  • Chris Strong: Tuesdays 7–8:30pm, Wednesday 6–7:30pm

Textbook and optional references

The textbook is Convex Optimization, available online, or in hard copy form at the Stanford Bookstore.

Course requirements and grading

Requirements:

  • Weekly homework assignments. We will use Gradescope. 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}.

  • Etude problems. There will be a weekly etude problem, to be done alone (solos, not duets, trios, quartets, or other ensembles). We will not accept late etude solutions; see the Etudes page for more information.

Grading: Homework 30%, etudes 70%. These weights are approximate; we reserve the right to change them later.

Prerequisites

Good knowledge of linear algebra (as in EE263), and exposure to probability. Exposure to numerical computing, optimization, and application fields helpful but not required; the applications will be kept basic and simple.

You will use one of CVX (Matlab), CVXPY (Python), CVXR (R), or Convex.jl (Julia) to write simple scripts, so basic familiarity with elementary programming will be required. We refer to CVX, CVXPY, CVXR, and Convex.jl collectively as CVX*.

Quizzes

This class has no formal quizzes. There are on-line quizzes on the lecture slides page. These are just for fun; they are not graded and your responses are not logged.

Catalog description

Concentrates on recognizing and solving convex optimization problems that arise in applications. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

Course objectives

  • to give students the tools and training to recognize convex optimization problems that arise in applications

  • to present the basic theory of such problems, concentrating on results that are useful in computation

  • to give students a thorough understanding of how such problems are solved, and some experience in solving them

  • to give students the background required to use the methods in their own research work or applications

Intended audience

This course should benefit anyone who uses or will use scientific computing or optimization in engineering or related work (e.g., machine learning, finance). More specifically, people from the following departments and fields: Electrical Engineering (especially areas like signal and image processing, communications, control, EDA & CAD); Aero & Astro (control, navigation, design), Mechanical & Civil Engineering (especially robotics, control, structural analysis, optimization, design); Computer Science (especially machine learning, robotics, computer graphics, algorithms & complexity, computational geometry); Operations Research (MS&E at Stanford); Scientific Computing and Computational Mathematics. The course may be useful to students and researchers in several other fields as well: Mathematics, Statistics, Finance, Economics.