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Nonlinear Programming, Spring 2004

(Complete Item Description)

Abstract:: This course introduces students to the fundamentals of nonlinear optimization theory and methods. 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.

Subject:: Business, Science and Technology
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

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Top Keywords

15.084J 6.252J Algorithmic Methods Include Steepest Descent Computer Sciences Conditional Gradient and Subgradient Optimization Electrical Engineering Generalized Programming Interior-Point Algorithms and Theory Interior-Point Methods and Penalty and Barrier Methods Lagrange and Conic Duality Theory Lagrangean Relaxation Linear and Quadratic Programming management Newton's Method Semi-Definite Programming Unconstrained and Constrained Optimization

Conditions of Use

No Strings Attached
Remix and Share
Share Only
Read the Fine Print

No restrictions on your remixing, redistributing, or making derivative works. Give credit to the author, as required.

Your remixing, redistributing, or making derivatives works comes with some restrictions, including how it is shared.

Your redistributing comes with some restrictions. Do not remix or make derivative works.

Copyrighted materials, available under Fair Use and the TEACH Act for US-based educators, or other custom arrangements. Go to the resource provider to see their individual restrictions.

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