EE364b - Convex Optimization II


  • Welcome to EE364b, Spring quarter 2018-19.

  • Annotated slides and animations are available on Canvas

  • Homework 1 is posted and due Friday 4/12

  • Homework 2 is posted and due Friday 4/19

  • Homework 3 is posted and due Friday 4/26

  • Homework 4 is posted and due Friday 5/3

  • Homework 5 is posted and due Friday 5/10

  • Homework 6 is posted and due Friday 5/17

  • Homework 7 is posted and due Friday 5/24

  • Homework 8 is posted and due Tuesday 6/4

  • Poster presentations will be on Jun 6, 17:00 in Packard atrium

  • Take-home final exam is released on Canvas. Please upload your submissions to Gradescope by Sunday June 9, 23:59 at the latest

Instructor: Mert Pilanci,

Course description

Continuation of 364A. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Monotone operators and proximal methods; alternating direction method of multipliers. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, machine learning and communications. Course requirements include a project or a final exam.


EE364a - Convex Optimization I