6.252 Nonlinear Optimization

Class Info

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

This class has 18.06, 18.100A, 18.100B, and 18.100Q as prerequisites.

6.252 will not be offered this semester. It will be available in the Spring semester, and will be instructed by R. M. Freund, P. Parrilo and G. Perakis.

Lecture occurs 11:00 AM to 12:30 PM on Tuesdays and Thursdays in E25-111.

This class counts for a total of 12 credits.

You can find more information on MIT OpenCourseWare at the Nonlinear Programming site.

MIT 6.252 Nonlinear Optimization Related Textbooks
MIT 6.252 Nonlinear Optimization On The Web
Nonlinear Programming

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