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

	Quadratic reformulations of nonlinear binary optimization problems Martin Anthony∗, Endre Boros†, Yves Crama‡, Aritanan Gruber§ Abstract Very large nonlinear unconstrained binary optimization problems DocID: 1prlq - View Document
	Quadratization of Symmetric Pseudo-Boolean Functions Martin Anthonya , Endre Borosb , Yves Cramac , Aritanan Gruberd,∗ a Department of Mathematics, London School of Economics and Political Science, UK. DocID: 1oMXE - View Document
	1 An Efficient Optimization Framework for Multi-Region Segmentation based on Lagrangian Duality Johannes Ul´en, Petter Strandmark and Fredrik Kahl DocID: 16wsv - View Document
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	Single- and Multi-Objective Genetic Programming: New Bounds for Weighted ORDER and MAJORITY Anh Nguyen Tommaso Urli DocID: PFJx - View Document