EE617: Linear and Convex Optimization (Fall 2017)

Instructor: Yuanzhang Xiao, xyz.xiao@gmail.com

Lectures: Monday and Wednesday 3:00pm - 4:15pm, Holmes Hall 242

Office Hours: Tuesday and Thursday 3:00pm - 4:15pm (or by appointment), POST Building 201G

Text Book: Convex Optimization by Stephen Boyd and Lieven Vandenberghe

Software: CVX in Matlab or CVXPY in Python

Grading Policy:

  • 5 homework assignments (50%)

  • mid-term exam (20%, open book)

  • final project (30%)

Schedule, Lecture Notes, and Reading

The schedule is subject to adjustment.

Theory (Aug. 21 - Sep. 20, 5 weeks)

  1. Introduction and Motivation (Lecture Notes, Read: Chapter 1)

  2. Convex Sets (Lecture Notes, Read: Chapter 2.1-2.3, 2.5)

  3. Convex Functions (Lecture Notes, Read: Chapter 3.1, 3.2, 3.4)

  4. Convex Optimization Problems (Lecture Notes, Read: Chapter 4.1-4.5)

  5. Optimality Condition and Duality (Lecture Notes, Read: Chapter 5.1-5.8)

Applications (Sep. 25 - Oct. 18, 4 weeks)

  1. Applications in Machine Learning

  2. Applications in Signal Processing

  3. Applications in Wireless Communications

  4. Applications in Smart Grids

Mid-Term Exam (Oct. 23)

Computation (Oct. 25 - Dec. 6, 6 weeks)

  1. CVX in Matlab and CVXPY in Python

  2. Unconstrained Minimization

  3. Equality Constrained Minimization

  4. Interior-Point Methods

Final Project (Dec. 11)

Homework

All homework problems are exercises in the textbook.

Homework 1 (due Oct. 23)

  • exercises 2.7, 2.11, 2.12 (a,b,c,d,e,g), 2.19, 2.24

  • exercises 3.2, 3.19, 3.20, 3.21, 3.23

Homework 2 (due Oct. 23)

  • exercises 4.7, 4.11, 4.15, 4.23, 4.33

  • exercises 5.5, 5.11, 5.21 (a,b,c), 5.26, 5.2