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Course Offerings

Course Evaluation Results

Course Details

Fall 2018-2019
* ORF 363 / COS 323 (QR)   Graded A-F, P/D/F, Audit

Computing and Optimization for the Physical and Social Sciences

Amir Ali Ahmadi

An introduction to several fundamental and practically-relevant areas of modern optimization and numerical computing. Topics include computational linear algebra, first and second order descent methods, convex sets and functions, basics of linear and semidefinite programming, optimization for statistical regression and classification, and techniques for dealing with uncertainty and intractability in optimization problems. Extensive hands-on experience with high-level optimization software. Applications drawn from operations research, statistics and machine learning, economics, control theory, and engineering.

Sample reading list:
K. P. Chong & Stanislaw H. Zak, An Introduction to Optimization, 4th ed.
Sanjoy Dasgupta, Christos Papadimitriou,Umesh Vazirani, Algorithms
R.J. Vanderbei, Linear Programming: Foundations and Extensions
Convex Optimization, S.Boyd, and L. Vandenberghe

Reading/Writing assignments:
Problem Sets and Design Projects

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

Other Requirements:
Statistical, design or other software use required

Prerequisites and Restrictions:
Multivariable Calculus: (e.g., MAT 201 or 203). Linear algebra (e.g. MAT 202 or 204). Basic familiarity with MATLAB..

Other information:
Students will use the MATLAB-based optimization software CVX/YALMIP, which is free to download

Reserved Seats:
Open to ORF Seniors Only 40

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
20457 L01 01:30:00 pm - 02:50:00 pm T Th   McCosh Hall   28   Enrolled:108 Limit:110 Closed