Course Website

No class on 9/5 (Labor Day), 10/10 (Midterm Recess), 11/21 (moved to Friday 11/11), 11/23 (moved to Friday 11/18)

Slides, program files, and homework assignments will be posted in the Owl Space at http://owlspace.rice.edu.

General announcement

Scope: Convex optimization problems arise in communication, system theory, VLSI, CAD, finance, inventory, network optimization, learning, computer vision, statistics, etc. Various new solvers are now available and have made solving convex problems ever easier. Nevertheless, problems are often unrecognized as convex and, therefore, remain unsolved. This course is designed to be an exposure to convex optimization problems, their solution techniques and applications.

Topics covered: optimization fundations, convex sets and functions, convex optimization problems (QP, SOCP, SDP, etc.), duality, a subset of algorithms, and applications.

Prerequisites: Linear algebra and MATLAB programming. No previous background in optimization is required.

Textbook: Convex Optimization by S.Boyd and L.Vendenberghe
Available both in print and online at www.stanford.edu/~boyd/cvxbook/

Course workload: about 3 - 4 homework sets including MATLAB programming assignments, 1 group project

Exceptional situations: Per the "No Note" policy, you should notify your instructor as soon as possible should any illness, emergency situation, travels, or other tasks arise that may cause you to miss the deadline for any assignment or exam. Extension requests will not be granted in case of late (e.g., last minute) notifications.

Grading: 20% class participation, 30% homework, 50% final project

Credits: 3 credit hours

Instructor: Wotao Yin, x5368, 3086 Duncan, wotao.yin at rice.edu
TA: TBA
Office Hour: TBA
Course webpage: www.caam.rice.edu/~wy1/CAAM565
Slides and homework: owlspace.rice.edu

Syllabus

A variety of applications will be discussed throughout the lectures. Below is a rough schedule.

Lectures 1-3: Introduction to optimization
Lectures 4-6: Recognizing problem structure convex sets and convex functions
Lectures 7-8: Formulations of convex optimization problems
Lectures 9-12: Duality theory
Lecture 13-14: Introduction to convex optimization algorithms
Lectures 15-20: Optimization for structured solutions (compressive sensing)
Lectures 21-29: Group presentations

Any student with a disability requiring accommodations in this class is encouraged to contact her/his instructor after class or during offi cehour s,a ndal so toconta cttheDisability Support Services officein the Student Center.

You should notify your instructor as soon as possible should any illness or emergency situation arise that may cause you to miss the deadline for any assignment or exam, as per the Student Health "No Note" policy.

Introduction slides from 2006

1. Introduction