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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.


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