Tuesday, April 15, 2014

COS 323


The description has changed so much since last year... New professor (like, literally new - he's coming to Princeton in the fall) who works with mathematical computation... It changed from recommended MAT 201/202 but not required, and no required prior experience with MATLAB to this. I'm scared.


Computing and Optimization for the Physical and Social Sciences

An introduction to several fundamental and practically-relevant areas of numerical computing with an emphasis on the role of modern optimization. Topics include computational linear algebra, descent methods, basics of linear and semidefinite programming, optimization for statistical regression and classification, trajectory optimization for dynamical systems, 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, finance, economics, control theory, and engineering.

Sample reading list:
M.T. HeathScientific Computing, an Introductory Survey (2nd edition)
T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. SteinIntroduction to Algorithms (2nd edition)
R.J. VanderbeiLinear Programming: Foundations and Extensions
Convex OptimizationS.Boyd, and L. Vandenberghe

Reading/Writing assignments:
Problem Sets and Design Projects 

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

Other Requirements:
Statistical, design or other software use required 

Prerequisites and Restrictions:
Multivariable Calculus: MAT 203 preferred; MAT 201 is acceptable. Linear Algebra: MAT 204 preferred; MAT 202 is acceptable. Familiarity with MATLAB. Familiarity with basics of probability and random variables (e.g., at the level of ORF 309) is recommended but not required.. 

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

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