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Course: STAT 31015
Title: Mathematical Computation IIA: Convex Optimization
Instructor(s): Mihai Anitescu
Teaching Assistant(s): Wanting Xu
Class Schedule: Sec 01: MW 2:30–3:50 PM in Eckhart 133
Office Hours:  
Textbook(s): Boyd, Vandenberghe, Convex Optimization
Description: This course covers the fundamentals of convex optimization. Topics will include basic convex geometry and convex analysis, KKT condition, Fenchel and Lagrange duality theory; six standard convex optimization problems and their properties and applications: linear programming, geometric programming, second-order cone programming, semidefinite programming, linearly and quadratically constrained quadratic programming. In the last part of the course we will examine the generalized moment problem --- a powerful technique that allows one to encode a wide variety of problems (in probability, statistics, control theory, financial mathematics, signal processing, etc) and solve them or their relaxations as convex optimization problems.

Prerequisite(s): STAT 30900/CMSC 37810