## EE364a: Course Information## LecturesLectures are Tuesdays and Thursdays, 10:30-11:50am, in NVIDIA auditorium. Videos of EE364a lectures will be available (to registered students) on SCPD, a few hours after each lecture, but you are encouraged to attend the live lectures. ## Office hours
Clara: Mondays, 9:15am – 11:15am, Packard 106 Tri: Mondays, 6pm – 8pm, Packard 106 Jenny: Tuesdays, 3pm – 4:50pm, Packard 106 Zhivko: Tuesdays, 7pm – 9pm, Packard 109 Keegan: Wednesdays, 9am – 11am, Packard 106 Enzo: Wednesdays, 4pm – 6pm, Packard 107 Khashayar: Wednesdays, 6pm – 8pm, Packard 106 Cheuk: Thursdays, 12:30pm – 2:30pm, Packard 104 Dieterich: Thursdays, 2pm – 4pm, Packard 106 Alex: Thursdays, 4pm – 6pm, 380-380X
## Textbook and optional referencesThe textbook is
## Course requirements and grading
*Weekly homework assignments*. Homework will normally be assigned each Friday, and due the following Friday by 5pm in the box 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 is 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.
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 matlab and CVX to write simple scripts, so some basic familiarity with matlab will be required. This year you also have the options of using CVXPY (Python) or Convex.jl (Julia). We refer to CVX, CVXPY, and Convex.jl collectively as CVX*. ## QuizzesThis class has ## 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. ## Course 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. |