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Polyhedral Approximations in Convex Optimization

Polyhedral Approximations in Convex Optimization

This video was recorded at NIPS Workshop on Optimization for Machine Learning, Whistler 2008. We propose a unifying framework for solution of convex programs by polyhedral approximation. It includes classical methods, such as cutting plane, Dantzig-Wolfe decomposition, bundle, and simplicial de- composition, but also includes refinements of these methods, as well as new methods that are well-suited for important large-scale types of problems, arising for example in network optimization.
Keywords:
videolectures, ocwc, oec
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More about this material

Material Type:
Presentation
Date Added to MERLOT:
February 10, 2015
Date Modified in MERLOT:
February 10, 2015
Author:
Submitter:
The Open Education Consortium
Primary Audience:
College General Ed, College Lower Division, College Upper Division
Technical Format:
Video
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States

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