ELEG 5481 Signal Processing Optimization Techniques

Course Description

Lecture Materials

Reference Books

Assignments & Projects

Tutorial

Information:

Assignment solution 5 has been posted.

Lecture notes on nonconvex optimization has been posted.

Lecture notes on pattern classification has been posted.

Assignment 5 has been posted.

The tutorial next week (Apr. 24) will be Q&A-based. You are welcomed to ask questions on your project, assignment, and other things related to the course. It will be SHB 329 from 4:30-5:15pm.

Tutorial solution 11 has been posted.

Lecture notes on sparse optimization has been posted.

Lecture notes on interior point method and subgradient method have been posted.

Tutorial 11 has been posted.

Assignment solution 4 has been posted.

Midterm solution has been posted.

Pls approach the tutor at Rm. 322 SHB to collect the midterm paper.

Lecture note on duality has been posted.

The tutorial on April 10 will be about filter design.

Tutorial solution 9 has been posted.

The project description for this semester has been uploaded.

Tutorial 9 has been posted.

Tutorial solution 8 has been posted.

Assignment 4 has been posted.

Lecture notes on second-order cone programming, semidefinite programming, and semidefinite relaxation have been posted.

Tutorial note 8 has been posted.

Notice the midterm examination this Wednesday, 7:00pm. You are allowed to bring with you the summary sheet. You are however not allowed to write anything on the summary sheet.

You are also not allowed to bring any other textbooks, notes or any electronic gadgets, except for simple calculators.

The lecture note on linear and quadratic programes has been updated.

Tutorial note 7 has been posted.

Tutorial solution 6 has been posted.

A summary on convex set and function has been posted.

Assignment solution 3 has been posted.

Lecture note on geometric programming has been posted.

Tutorial 6 has been posted.

Assignment solution 2 has been posted.

Tutorial solution 5 has been posted.

Tutorial 5 has been posted.

Lecture notes on convex optimizations and linear and quadratic programs have been posted.

The project description in 2010 has been posted. Note that this is for reference only.

Assignment 3 has been posted.

Assignment solution 1 has been posted.

Tutorial solution 4 has been posted.

Tutorial solution 3 has been posted.

Tutorial 4 has been posted.

Tutorial 3 has been posted.

Homework 2 has been posted.

Tutorial solution 2 has been posted.

Tutorial 2 has been posted.

Homework 1 has been posted.

A matlab demo on total variation reconstruction has been posted.

Lecture notes on convex function have been posted.

Tutorial solution 1 has been posted.

Tutorial note 1 has been posted.

There will be tutorial starting from Jan. 30, 2013. Venue: ERB 405, Time: every Wed. 4:30-5:15pm. Note that the tutorial is optional. It means to provide exercises and additional examples for those who want to get themselves familiarized with linear algebra, sets, functions and the sort.

The first lecture is on Jan. 16, at ERB 405.

Lecturer-in-Charge:

Dr. Wing-Kin (Ken) Ma, Room 323, Ho Sin Hang Engineering Building, Tel 39434350, wkma@ee.cuhk.edu.hk

Tutor:

Mr. Jiaxian Pan , Room 329, Ho Sin Hang Engineering Building, Tel 39438266 jxpan@ee.cuhk.edu.hk

Lecture:

Time Venue

Wednesday 19:00 – 22:00 William M W Mong Eng Bldg 405

Tutorial:

Time Venue

Wednesday 16:30 – 17:15 William M W Mong Eng Bldg 405

Lecture Materials

1. Introduction

Lecture slides
Matlab demo codes: FIR filter design, L_1 reconstruction. Note that you must install CVX on your PC in order to run them

2. Basics: linear algebra, matrix analysis and functions

read the Convex Optimization textbook, Apendix A. You may also take a look at my handwritten note which is more or less the same.
Supplementary materials: some lecture slides of a previously taught course on linear algebra and matrix analysis

Lecture 1: Basic concepts

Lecture 2: Eigenvalues and eigenvectors

Lecture 4: Singular value decomposition and orthogonal projection

Lecture 5: Positive semidefinite matrices

Lecture 8: Least squares

(New) matlab demo: total variation (TV) reconstruction (matlab file). See lecture slides 17-18 in Stephen Boyd's course website for a problem description

3. Convex sets

Read the Convex Optimization textbook, chapter 2, 2.1-2.3. Here are my handwritten note (part 1, part 2), which are again heavily based on the textbook.
Additional references: Lecture slides for my convex optimization short course in 2008. You can also find similar lecture slides in Stephen Boyd's course website.

4. Convex Functions