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6.252J is a course in the department’s “Communication, Control, and Signal Processing” concentration.
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English
English [CC]
FREE
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Description
This course provides a unified analytical and computational approach to nonlinear optimization problems. The topics covered in this course include: unconstrained optimization methods, constrained optimization methods, convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. There is also a comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory. Throughout the course, applications are drawn from control, communications, power systems, and resource allocation problems.
Course content
- Introduction Unlimited
- Unconstrained Optimization – Optimality Conditions Unlimited
- Gradient Methods Unlimited
- Convergence Analysis of Gradient Methods Unlimited
- Rate of Convergence Unlimited
- Newton and Gauss – Newton Methods Unlimited
- Additional Methods Unlimited
- Optimization Over a Convex Set; Optimality Conditions Unlimited
- Feasible Direction Methods Unlimited
- Alternatives to Gradient Projection Unlimited
- Constrained Optimization; Lagrange Multipliers Unlimited
- Constrained Optimization; Lagrange Multipliers Unlimited
- Inequality Constraints Unlimited
- Introduction to Duality Unlimited
- Interior Point Methods Unlimited
- Penalty Methods Unlimited
- Augmented Lagrangian Methods Unlimited
- Duality Theory Unlimited
- Duality Theorems Unlimited
- Strong Duality Unlimited
- Additional Dual Methods Unlimited
Instructor
Massachusetts Institute of Technology
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