EE364a: Course Information

Lecture

Lecture is on Tuesdays and Thursdays, 9:30–10:45am, in room 420-040. EE364a will not be televised this year.

Several sets of videos of previous lectures are available, but should not be considered a substitute for coming to class.

Office hours

Stephen Boyd’s office hours: Tuesdays 10:45–12 and 1:15–2:15, Packard 264.

TA Office hours: (starting second week of classes; more to be announced)

• Tuesdays, 2–4 in Packard 104.

• Wednesdays, 4–6 in Packard 364.

• Wednesdays, 6:15–8:15 in Packard 104.

• Thursdays, 3–5 in Packard 106.

Textbook and optional references

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

Several texts can serve as auxiliary or reference texts:

• Bertsekas, Nedic, and Ozdaglar, Convex Analysis and Optimization

• Ben-Tal and Nemirovski, Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications

• Nesterov, Introductory Lectures on Convex Optimization: A Basic Course

• Ruszczynski, Nonlinear Optimization

• Borwein & Lewis, Convex Analysis and Nonlinear Optimization

You won’t need to consult them unless you want to.

Course requirements and grading

Requirements:

• Weekly homework assignments. Homework will normally be assigned each Friday and due the following Friday by 5 pm in the inbox across from Packard 243. 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. Homework will be graded on a scale of 0–4.

• Final exam. The format it a 24 hour take home, scheduled for the last week of classes, but we will accommodate your schedule if you can’t take it at that time.

Grading: Homework 20%, final 80%. 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.

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.