## EE364a: Convex Optimization IEE364a is the same as CME364a. ## Announcements## InstructorProfessor Stephen Boyd's office hours: TBD
## Teaching assistantsTBD
Office hours: TBD
SCPD Office Hours: TBD
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 ## Requirements*Weekly homework assignments*, due each Friday at midnight, starting the second week. 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}.
*Midterm quiz*. The format is a timed 75 minute exam, during the 4th week. You can take the midterm quiz anytime beginning after Thursday, 7/14 at 9am and ending before Saturday, July 16th at 1pm. The midterm quiz covers chapters 1–3, and the concept of disciplined convex programming (DCP). Here's the 2019 midterm with solutions for practice. And here's the 2022 midterm and solutions. And here's the 2022 summer midterm and solutions.
*Final exam*. The format is a 24 hour take home exam, scheduled for the last week of classes. You can take it during any 24 hour period beginning after 12:00pm Thursday August 11th and ending by 12:00pm Saturday August 13th. We can arrange for you take it earlier (as a beta tester, and only if you really need to) but not later. There will be some flexibility on when you take it.
## 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. ## Objectivesto 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 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. |