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Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to solve exactly. The purpose of this book is to provide in a single volume, major algorithmic aspects and applications of NAPs as contributed by leading international experts.
The chapters included in this book are concerned with major applications and the latest algorithmic solution approaches for NAPs. Approximation algorithms, polyhedral methods, semidefinite programming approaches and heuristic procedures for NAPs are included, while applications of this problem class in the areas of multiple-target tracking in the context of military surveillance systems, of experimental high energy physics, and of parallel processing are presented.
Audience: Researchers and graduate students in the areas of combinatorial optimization, mathematical programming, operations research, physics, and computer science.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Contenuti
List of Figures. List of Tables. Preface. Contributing Authors. Introduction; P. Pardalos, L. Pitsoulis. 1. Multi Index Assignment Problems: Complexity, Approximation, Applications; F.C.R. Spieksma. 2. MD Assignment of Data Association; A.B. Poore. 3. Target-Based Weapon Target Assignment Problems; R.A. Murphey. 4. The Nonlinear Assignment Problem in Experimental High Energy Physics; J.-F. Pusztaszeri. 5. Three Index Assignment Problem; L. Qi, D. Sun. 6. Polyhedral Methods for the QAP; V. Kaibel. 7. Semidefinite Programming Approaches to the Quadratic Assignment Problem; H. Wolkowicz. 8. Heuristics for Nonlinear Assignment Problems; S. Voss. 9. Symbolic Scheduling of Parameterized Task Graphs on Parallel Machines; M. Cosnard, et al. 10. Decomposition Algorithms for Communication Minimization in Parallel Computing; I.T. Christou, R.R. Meyer.
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Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to so. Codice articolo 5969581
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to solve exactly. The purpose of this book is to provide in a single volume, major algorithmic aspects and applications of NAPs as contributed by leading international experts. The chapters included in this book are concerned with major applications and the latest algorithmic solution approaches for NAPs. Approximation algorithms, polyhedral methods, semidefinite programming approaches and heuristic procedures for NAPs are included, while applications of this problem class in the areas of multiple-target tracking in the context of military surveillance systems, of experimental high energy physics, and of parallel processing are presented. Audience: Researchers and graduate students in the areas of combinatorial optimization, mathematical programming, operations research, physics, and computer science. 332 pp. Englisch. Codice articolo 9780792366461
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Nonlinear Assignment Problems
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Buch. Condizione: Neu. Neuware -Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to solve exactly. The purpose of this book is to provide in a single volume, major algorithmic aspects and applications of NAPs as contributed by leading international experts.The chapters included in this book are concerned with major applications and the latest algorithmic solution approaches for NAPs. Approximation algorithms, polyhedral methods, semidefinite programming approaches and heuristic procedures for NAPs are included, while applications of this problem class in the areas of multiple-target tracking in the context of military surveillance systems, of experimental high energy physics, and of parallel processing are presented.Audience: Researchers and graduate students in the areas of combinatorial optimization, mathematical programming, operations research, physics, and computer science. 332 pp. Englisch. Codice articolo 9780792366461
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to solve exactly. The purpose of this book is to provide in a single volume, major algorithmic aspects and applications of NAPs as contributed by leading international experts. The chapters included in this book are concerned with major applications and the latest algorithmic solution approaches for NAPs. Approximation algorithms, polyhedral methods, semidefinite programming approaches and heuristic procedures for NAPs are included, while applications of this problem class in the areas of multiple-target tracking in the context of military surveillance systems, of experimental high energy physics, and of parallel processing are presented. Audience: Researchers and graduate students in the areas of combinatorial optimization, mathematical programming, operations research, physics, and computer science. Codice articolo 9780792366461
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Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
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