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This course introduces students to the fundamentals of nonlinear optimization theory and methods.
0
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English
English [CC]
FREE
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Description
Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangian relaxation, generalized programming, and semi-definite programming. Algorithmic methods used in the class include steepest descent, Newton’s method, conditional gradient and subgradient optimization, interior-point methods and penalty and barrier methods.
Course content
- Unconstrained Optimization Optimality Conditions Unlimited
- Newton’s Method Unlimited
- Quadratic Forms Unlimited
- Constrained Optimization Optimality Conditions I Unlimited
- Projection Methods for Equality Constrained Problems Unlimited
- Projection Methods/Penalty Methods Unlimited
- Barrier Methods, Conditional Gradient Method Unlimited
- Interior-Point Methods for Linear Optimization I Unlimited
- Analysis of Convex Sets Unlimited
- Duality Theory I Unlimited
- Duality Theory IV Unlimited
- Generalized Programming and Subgradient Optimization Unlimited
- Semidefinite Optimization I Unlimited
Instructor
Massachusetts Institute of Technology
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