ANNOUNCEMENTS:

COURSE DESCRIPTION:

Convex optimization is a form of non-linear optimization that includes linear programming and least squares as special cases. Like linear programming and least squares, convex optimization has a fairly complete theory, very efficient algorithms, and a wide range of applications. Application areas include computer science, engineering, statistics, finance, economics and operations research.

This course is an introduction to the theory, algorithms and applications of convex optimization. The goal is to give students a working knowledge of the subject, i.e., the ability to recognize, formulate, and solve convex optimization problems. Topics covered will be selected from the following: convex sets and functions, linear and quadratic optimization, geometric and semidefinite programming, strong and weak duality, algorithms for constrained and unconstrained problems, interior point methods, and applications. The course should be of special interest to students in machine learning, machine vision, graphics, numerical analysis, combinatorial optimization and electrical engineering.

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Background material:

Matlab:

Octave:

Plagiarism and Cheating: