Lectures are Tuesdays and Thursdays, 1:15-3:05pm, in Thornton 102. Thursdays will be interactive sessions in which students will discuss and present on homework problems. Students will also be required to watch some lectures online in order to make time for the more interactive class sessions on Thursdays.
Several sets of videos of previous lectures are available, but should not be considered a substitute for coming to class.
Madeleine Udell's office hours: Mondays 5 – 6:15pm, Packard 243, and by appointment.
TA office hours: The TAs will offer informal working sessions, that will also serve as their office hours, during the week. Attendance is not required.
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:
You won't need to consult them unless you want to.
Course requirements and grading
Grading: Homework 20%, problem presentations 10%, participation 20%, final 50%.
These weights are approximate; we reserve the right to change them later.
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 matlab and CVX to write simple scripts, so some basic familiarity with matlab will be required. Many good matlab tutorials are available online. The short course “CME192: Introduction to Matlab” is offered concurrently. This course is entirely optional, and will cover matlab in much greater depth than we require.
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.
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.
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.