Topics in Convex Optimization

August 25-26, 2011

In cooporation with CADICS

General Information

The course is given August 25-26 by Stephen Boyd and it will be held at the department of Automatic Control

Contents

• See the literature below

Organization and Examination

• Lectures: 9.00-17.00 both days with lunch break 12-13
• Location: Visionen, Building B, Ground Floor (Level 2), Campus Valla, Linköping. Map
• Course credits: 2hp
• Examination: Complete the homework assignments
• Homework assignment 1: Exercise 3 in Homework 7 which is found here. This exercise need not to be handed in.
• Homework assignment 2:

  Solve the problem:

  minimize 1/2||Ax-b||^2_2
  

  s.t. ||x||_{\infty}<=1.
  

  where A\in R^{m \times n}, m>n
  

The solution should use factorization caching
Compare to Matlab x = A\b
Optional Use sparse A; Use the LSQR routine
Solutions should be working code with comments explaining the necessary details. Send your solution to ljung_at_isy.liu.se

Course Literature

Suggested course contents and relevant links

• A paper on ADMM http://www.stanford.edu/~boyd/papers/admm_distr_stats.html
• Lectures 8--13 of EE364b http://www.stanford.edu/class/ee364b/lectures.html
• Code generation for embedded optimization

  http://stanford.edu/~boyd/papers/code_gen_impl.html

  http://stanford.edu/~boyd/papers/code_gen_rhc.html

  http://stanford.edu/~boyd/papers/rt_cvx_sig_proc.html

  http://stanford.edu/~boyd/papers/rt_cvx_opt.html
  

Prerequisites

Optional: Course in Convex Optimization and/or Machine Learning

Page responsible: Torkel Glad
Last updated: 2023-08-27