EE364a: Convex Optimization I

EE364a is the same as CME364a.

This webpage contains basic course information; up to date and detailed information is on Ed.


  • Welcome to EE364a, Winter quarter 2023–2024.

  • EE364a will be taught by Stephen Boyd and Babak Ayazifar.

  • Lectures are Tuesdays and Thursdays 10:30–11:50AM, NVIDIA AUD. The first lecture is January 9.

  • We'll post more information as we get closer to the start of Winter quarter.

  • If you're looking for something to do before class starts, you could read Chapter 1 of the textbook, or install CVXPY.

  • The course will be on SCPD, so videos of the lectures will be available to enrolled students.

Course staff

Course assistants:

TA office hours and locations will be announced on Ed.


The textbook is Convex Optimization, available online, or in hard copy from your favorite book store.


  • Weekly homework assignments, due each Friday at midnight, starting the second week. We will use Gradescope for homework submission, with the details on Ed. We will have a late day policy on homeworks. Each student has one late day, i.e., you may submit one homework (except for homework 0) up to 24 hours late. Always reach out if you're facing unusual disruptions to your classwork. 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}.

  • Midterm quiz. The format is a timed online 75 minute exam, at the end of the 4th week. The midterm quiz covers chapters 1–3, and the concept of disciplined convex programming (DCP).

  • Final exam. The format is a 24 hour take home exam, scheduled for the end of the last week of classes. You can take it during any 24 hour period over a multi-day period we'll fix later. We can arrange for you take it earlier (as a beta tester, and only if you really need to) but not later. The final exam will require the use of CVXPY.


Homework 20%, midterm 15%, final exam 65%. These weights are approximate; we reserve the right to change them later.


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

You will use CVXPY to write simple scripts, so basic familiarity with elementary Python programming is required. We will not be supporting other packages for convex optimization, such as Convex.jl (Julia), CVX (Matlab), and CVXR (R). In particular, the final exam will require the use of CVXPY.

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.


  • 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.