Syllabus

A computational and application-oriented introduction to the modeling of largescale systems in a wide variety of decision-making domains and the optimization of such systems using state-of-the-art optimization software. Application domains include transportation and logistics, pattern classification, structural design, financial engineering, and telecommunications system planning. Modeling tools and techniques covered include linear, network, discrete, and nonlinear optimization, heuristic methods, sensitivity and postoptimality analysis, decomposition methods for large-scale systems, and stochastic optimization. This course is oriented around computation and computation-related issues in developing and solving large-scale optimization models.

MIT subject Optimization Methods or Introduction to Mathematical Programming /Introduction to Mathematical Programming, or permission of instructor

Text: Introduction to Linear Optimization, D. Bertsimas and J. Tsitsiklis, Athena Scientific, Belmont, MA, 1997 (BT in syllabus), on reserve at Dewey Library (MIT) and at Central Library (NUS). Also, several copies of the book The OPL Optimization Programming Language, by Pascal Van Hentenryck (MIT Press, 1999), are on reserve in Dewey Library.

MIT recitations for the course will be held on Fridays. Voluntary attendance at recitations is encouraged but not required.

Seven Problem Sets: 25%
Midterm Exam: 30%
Final Exam: 35%
Class Interaction: 10%