Course Website
The course website resides in the Owl Space at http://owlspace.rice.edu. All slides, codes, and homework assignments will be posted in the Owl Space.
CAAM 554: Convex Optimization, Fall 2007
Tuesday and Thursday, 10:50am - 12:05pm, MART 101
Instructor: Wotao Yin
General announcement
Scope: Convex optimization problems arise in communication, system theory, VLSI, CAD, finance, inventory, network optimization, learning, computer vision, statistics, etc. Thanks to the recent advances in interior-point methods, 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: convex sets and functions, abstract convex optimization problems (QP, SOCP, SDP, etc.), duality, interior-point algorithms, and applications from
· CS, EE, and BioEng: statistical learning, image processing, computer vision, etc.
· Business: robust portfolio selection, etc.
· Statistics: robust regression, data fitting, etc.
Prerequisites: Linear algebra. Some knowledge in analysis, probability, and Matlab programming helps. No previous background in linear or nonlinear 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: approx. 5 homework sets, 1 take-home midterm exam (tentative), a few Matlab programming assignments, 1 group project
Credits: 3 credit hours
Instructor: Wotao Yin
Office Hour: 10:00-10:50 a.m. Tuesday and Thursday
Course webpage: www.caam.rice.edu/~wy1/CAAM554
Slides and homework: owlspace.rice.edu
Course flyer (made for Fall 2006) [link]
Topics covered: convex sets and functions, abstract convex optimization problems (QP, SOCP, SDP, etc.), duality, interior-point algorithms, and applications from
· CS, EE, and BioEng: statistical learning, image processing, computer vision, etc.
· Business: robust portfolio selection, etc.
· Statistics: robust regression, data fitting, etc.
Prerequisites: Linear algebra. Some knowledge in analysis, probability, and Matlab programming helps. No previous background in linear or nonlinear 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: approx. 5 homework sets, 1 take-home midterm exam (tentative), a few Matlab programming assignments, 1 group project
Credits: 3 credit hours
Instructor: Wotao Yin
Office Hour: 10:00-10:50 a.m. Tuesday and Thursday
Course webpage: www.caam.rice.edu/~wy1/CAAM554
Slides and homework: owlspace.rice.edu
Course flyer (made for Fall 2006) [link]
Syllabus
15 weeks.
Lectures 1-2: Introduction
Lectures 3-7: Recognizing convex sets and convex functions
Lectures 8-11: Formulations of convex optimization problems
Lectures 11-14: Duality theory
Lecture 15: Introduction to convex optimization algorithms
Lectures 16-17: Matlab programming basic and tricks
Lecture 18: Group project proposals
Lectures 19-25: Convex optimization algorithms
Lectures 27-29: Student project presentations
A variety of applications are studied throughout the lectures.
Lectures 1-2: Introduction
Lectures 3-7: Recognizing convex sets and convex functions
Lectures 8-11: Formulations of convex optimization problems
Lectures 11-14: Duality theory
Lecture 15: Introduction to convex optimization algorithms
Lectures 16-17: Matlab programming basic and tricks
Lecture 18: Group project proposals
Lectures 19-25: Convex optimization algorithms
Lectures 27-29: Student project presentations
A variety of applications are studied throughout the lectures.
Copyright © 2007 Wotao Yin