R Foundations and Trends in Machine Learning

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Date: 2013-10-07 09:14:41 Convex optimization Submodular set function Ellipsoid method Linear programming Pseudo-Boolean function Subgradient method Optimization problem Convex analysis Combinatorial optimization Mathematical optimization Mathematical analysis Operations research		R Foundations and Trends in Machine Learning Add to Reading List Source URL: www.di.ens.fr Download Document from Source Website File Size: 2,05 MB Share Document on Facebook

	Hausdorﬀ Center for Mathematics, Summer School (May 9–13, 2016) Problems for “Discrete Convex Analysis” (by Kazuo Murota) Problem 1. Prove that a function f : Z2 → R defined by f (x1 , x2 ) = φ(x1 − x2 ) is DocID: 1vjVY - View Document
	How elegant modern convex analysis was influenced by Moreau’s seminal work. Samir ADLY University of Limoges, France DocID: 1vhAg - View Document
	December 8, 2016 Errata to Kazuo Murota, Akiyoshi Shioura, and Zaifu Yang: “Time Bounds for Iterative Auctions: A Unified Approach by Discrete Convex Analysis” DocID: 1vbMj - View Document
	Hausdorff School: Economics and Tropical Geometry Bonn, May 9-13, 2016 Discrete Convex Analysis III: Algorithms for Discrete Convex Functions Kazuo Murota DocID: 1v6lO - View Document
	Operator Splitting Methods for Convex Optimization Analysis and Implementation Goran Banjac St Edmund Hall DocID: 1v2Df - View Document