EE364a: Convex Optimization IInstructor Eric Luxenberg,
Stanford University
EE364a is the same as CME364a. Announcements
InstructorEric Luxenberg. Office hours:
Teaching assistants
Office hours:
SCPD Office Hours:
Links for Zoom office hours are available on the calendar in Canvas. LecturesLectures are Tuesdays and Thursdays, 9:45–11:15am, in Thornton 102. Videos of lectures will appear in Panopto on Canvas. You can watch the lectures live using via Zoom using the link on the calendar in Canvas. If you're not an enrolled student, you can watch videos from earlier years at Stanford Engineering Everywhere or YouTube. Contacting usWe will host the discussion forum in Ed. You can also contact the course staff at the staff email address. (Please do not use the Instructor's or the TAs’ direct email addresses for matters related to the course.) TextbookThe textbook is Convex Optimization, available online, or in hard copy from your favorite book store. Requirements
GradingHomework 20%, midterm 15%, final exam 65%. These weights are approximate; we reserve the right to change them later. PrerequisitesGood 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 CVXPY (Python) or Convex.jl (Julia) to write simple scripts, so basic familiarity with elementary programming is required. You could also use CVX (Matlab), or CVXR (R), but we won't be supporting these. We refer to CVXPY, Convex.jl, CVX, and CVXR collectively as CVX*. Catalog descriptionConcentrates 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. Objectives
Intended audienceThis 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. |