## EE364a: Course Information## LecturesLectures are Tuesdays and Thursdays, 9:00–10:20am, 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
Akshay: Tuesday 10:30am-12:30pm, 380-381T Hao: Tuesday 1pm-3pm, EDUC 334 Rachel: Tuesday 3pm-5pm, Herrin T185 Junzi: Tuesday 6pm-8pm, 320-107 Amr: Wednesday 10am-12pm, Herrin T185 Soroosh: Wednesday 3pm-5pm, Herrin T195 [SCPD through Google Hangout, link posted on piazza] Moosa: Thursday 12pm-2pm, Herrin T195 Qingyun: Thursday 1pm-3pm, 380-380X Guillermo: Thursday 2pm-4pm, Herrin T195 Ahmadreza: Thursday 4pm-6pm, Hewlett 101 [SCPD through Google Hangout, link posted on piazza]
## 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 on 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. 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 one of CVX (Matlab), CVXPY (Python), or Convex.jl (Julia), to write simple scripts, so basic familiarity with elementary programming will be required. 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. |