6.252 Nonlinear Optimization
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.
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.
© Copyright 2015 Yasyf Mohamedali