ELE522: Large-Scale Optimization for Data Science

Yuxin Chen, Princeton University, Fall 2019


  • Yuxin Chen, C330 Equad, yuxin dot chen at princeton dot edu

    • Office hours: Mon 4:30PM - 6:00PM (after the lectures), C330 Equad, or by appointment

Teaching Assistant

  • Yuling Yan, 213 Sherrerd hall, yulingy at princeton dot edu

    • Office hours: Tue 2:30-3:30pm, Sherrerd 005, or by appointment

Lecture Times

  • Mon, Wed 3:00 PM - 4:20 PM


  • Sherrerd hall 001

Administrative Assistant


  • Homeworks. There will be a few homework assignments that involve both theory and programming components. A hard copy of your homework must be turned in at the beginning of class. No late homeworks are accepted. You are encouraged to use LaTeX to typeset your homeworks.

  • Final exam. This will be a taken-home exam.

  • Course project. See the description here.

Course Policies

  • Prerequisites. Students should have backgrounds in basic linear algebra and in basic probability (measure-theoretic probability is not needed), as well as knowledge of a programming language like MATLAB or Python to conduct simple simulation exercises. Students should also know the basic notion of convexity and optimization; a course such as ORF307 (Optimization) would be beneficial.

  • Piazza. The main mode of electronic communication between students and staff, as well as amongst students, will be through http:www.piazza.com/. It is intended for general questions about the course, clarifications about assignments, student questions to each other, discussions about material, and so on. We strongly encourage students to participate in discussion, ask and answer questions through this site. The course staff will monitor discussions closely.

  • Collaboration. We encourage you to work on homework problems in study groups. However, you must write up and submit your own solutions and code without reading or copying the solutions of other students or other online resources.