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Titlebook: Decision Diagrams for Optimization; David Bergman,Andre A. Cire,John Hooker Book 2016 The Editor(s) (if applicable) and The Author(s), und

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書目名稱Decision Diagrams for Optimization
編輯David Bergman,Andre A. Cire,John Hooker
視頻videohttp://file.papertrans.cn/265/264187/264187.mp4
概述Presents a new theoretical and algorithmic approach to discrete optimization.Authors among leading researchers in this domain.Useful for researchers and practitioners in discrete optimization and cons
叢書名稱Artificial Intelligence: Foundations, Theory, and Algorithms
圖書封面Titlebook: Decision Diagrams for Optimization;  David Bergman,Andre A. Cire,John Hooker Book 2016 The Editor(s) (if applicable) and The Author(s), und
描述.This book introduces a novel approach to discrete optimization, providing both theoretical insights and algorithmic developments that lead to improvements over state-of-the-art technology. The authors present chapters on the use of decision diagrams for combinatorial optimization and constraint programming, with attention to general-purpose solution methods as well as problem-specific techniques...The book will be useful for researchers and practitioners in discrete optimization and constraint programming.?.".Decision Diagrams for Optimization is one of the most exciting developments emerging from constraint programming in recent years. This book is a compelling summary of existing results in this space and a must-read for optimizers around the world.." [Pascal Van Hentenryck].
出版日期Book 2016
關(guān)鍵詞Constraint Programming; Cutting Planes; Decision Diagrams; Discrete Optimization; Graph Coloring; Operati
版次1
doihttps://doi.org/10.1007/978-3-319-42849-9
isbn_softcover978-3-319-82679-0
isbn_ebook978-3-319-42849-9Series ISSN 2365-3051 Series E-ISSN 2365-306X
issn_series 2365-3051
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-1-4615-0286-9 models can be used in a top-down compilation method to construct exact decision diagrams. We also present an alternative compilation method based on constraint separation. We illustrate our framework on a number of classical combinatorial optimization problems: maximum independent set, set covering
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Data Mining and Knowledge Discovery Handbookd diagram can be perceived as a counterpart of the concept of relaxed diagrams introduced in previous chapters, and represents an under approximation of the feasible set, the objective function, or both. We first show how to modify the top-down compilation approach to generate restricted diagrams th
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Dimension Reduction and Feature Selectionpact of variable ordering on the size of exact decision diagrams for the maximum independent set problem. We provide worst-case bounds on the size of the exact decision diagram for particular classes of graphs. For general graphs, we show that the size is bounded by the Fibonacci numbers. Lastly, we
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