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: approx. 4 homework sets, a few MATLAB programming assignments, 1 final take-home exam

Grading: 40% class participation, 30% homework, 30% take-home final

Credits: 3 credit hours

Instructor: Wotao Yin, x5368, 3086 Duncan, wotao.yin at rice.edu (my replies often get tagged SPAM by Rice's SPAM filter, so check your SPAM box)
TA: TBA
Office Hour: TBA
Course webpage: www.caam.rice.edu/~wy1/CAAM554
Slides and homework: owlspace.rice.edu