6.252J -- Nonlinear Programming
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.

This class is at the Graduate level
This course is also known as: 15.084J
Instructor: R. M. Freund, D. P. Bertsekas, G. Perakis
Open Courseware Website
Prerequisites: 18.06, 18.100

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