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Course Details

Spring 2016-2017
ORF 523   Graded A-F, P/D/F, Audit

Convex and Conic Optimization

Amir Ali Ahmadi

A mathematical introduction to convex, conic, and nonlinear optimization. Topics include convex analysis, duality, theorems of alternatives and infeasibility certificates, semidefinite programming, polynomial optimization, sum of squares relaxation, robust optimization, computational complexity in numerical optimization, and convex relaxations in combinatorial optimization. Applications drawn from operations research, dynamical systems, statistics, and economics.

Sample reading list:
Ben-Tal and Nemirovski, Lectures on Modern Convex Optimization
Boyd and Vandenberghe, Convex Optimization
R. J. Vanderbei, Linear programming Foundations and Extensions
M. Laurent and F. Vallentin, Semidefinite Optimization

Requirements/Grading:
Mid Term Exam - 20%
Take Home Final Exam - 30%
Problem set(s) - 50%

Other Requirements:
Open to Graduate Students Only.

Prerequisites and Restrictions:
Prerequisite: linear optimization.

Website:  http://aaa.princeton.edu/orf523

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
40416 L01 1:30 pm - 2:50 pm Th   Julis Romo Rabinowitz Building   A98   Enrolled:27 Limit:35
40416 L01 1:30 pm - 2:50 pm T   Julis Romo Rabinowitz Building   198   Enrolled:27 Limit:35