Nonlinear Programming >> Content Detail



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6.252J: Non Linear Programming

Spring 2003

Two sessions / week
1.5 hours / session

Alternating Weeks
1 hour / session

Professor Dimitri P. Bertsekas

Course Description: A unified analytical and computational approach to nonlinear optimization problems. Unconstrained optimization methods include gradient, conjugate direction, Newton, and quasi-Newton methods. Constrained optimization methods include feasible directions, projection, interior point, and Lagrange multiplier methods. Convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. Comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory. Applications drawn from control, communications, power systems, and resource allocation problems.

Text: Bertsekas. Nonlinear Programming: 2nd Edition. Belmont, MA: Athena Scientific , 1999. ISBN: 1886529000.

In-class midterm (30%)
3-hour final (40%)
Problem Sets (30%)



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