Restless Bandit Models for Sensor Management: Designing nearly-optimal and tractable sensor scheduling rules

 
9783659283307: Restless Bandit Models for Sensor Management: Designing nearly-optimal and tractable sensor scheduling rules

Mathematical models for the optimal allocation of resources over time under uncertainty are notoriously difficult to solve, both for computational and technical reasons. This book addresses two complex stochastic and dynamic optimization problems, with application in modern sensor systems: (i) hunting multiple elusive targets and (ii) tracking multiple moving targets. These problems are formulated as Multi-armed Restless Bandit models with continuous state, which augments the technical difficulties to derive and compute its optimal solution and motivates the focus on designing tractable and well-performing heuristic policies of priority-index type instead. The book presents how to derive such policies based on a Lagrangian relaxation and decomposition approach. The contents of this book are of relevance both for applied and methodological communities. For modern sensor systems' operators, it provides nearly-optimal and tractable novel scheduling rules. For researchers concerned with the design of near-optimal solution policies for Multi-armed Restless Bandit Problems, it deploys a systematic procedure to do so based on recent extensions of indexation theory.

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About the Author:

Sofía S. Villar is a postdoctoral fellow at the Basque Center for Applied Mathematics (BCAM), Spain. She earned her Ph.D. (2012) and Master (2008) in Business Administration and Quantitative Methods at Universidad Carlos III de Madrid. She obtained her bachelor degree in Economics (2004) from Universidad Nacional de Córdoba (Argentina).

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Book Description Book Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Designing nearly-optimal and tractable sensor scheduling rules | Mathematical models for the optimal allocation of resources over time under uncertainty are notoriously difficult to solve, both for computational and technical reasons. This book addresses two complex stochastic and dynamic optimization problems, with application in modern sensor systems: (i) hunting multiple elusive targets and (ii) tracking multiple moving targets. These problems are formulated as Multi-armed Restless Bandit models with continuous state, which augments the technical difficulties to derive and compute its optimal solution and motivates the focus on designing tractable and well-performing heuristic policies of priority-index type instead. The book presents how to derive such policies based on a Lagrangian relaxation and decomposition approach. The contents of this book are of relevance both for applied and methodological communities. For modern sensor systems' operators, it provides nearly-optimal and tractable novel scheduling rules. For researchers concerned with the design of near-optimal solution policies for Multi-armed Restless Bandit Problems, it deploys a systematic procedure to do so based on recent extensions of indexation theory. | Format: Paperback | Language/Sprache: english | 180 pp. Bookseller Inventory # K9783659283307

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Book Description LAP Lambert Academic Publishing Nov 2012, 2012. Taschenbuch. Book Condition: Neu. Neuware - Mathematical models for the optimal allocation of resources over time under uncertainty are notoriously difficult to solve, both for computational and technical reasons. This book addresses two complex stochastic and dynamic optimization problems, with application in modern sensor systems: (i) hunting multiple elusive targets and (ii) tracking multiple moving targets. These problems are formulated as Multi-armed Restless Bandit models with continuous state, which augments the technical difficulties to derive and compute its optimal solution and motivates the focus on designing tractable and well-performing heuristic policies of priority-index type instead. The book presents how to derive such policies based on a Lagrangian relaxation and decomposition approach. The contents of this book are of relevance both for applied and methodological communities. For modern sensor systems' operators, it provides nearly-optimal and tractable novel scheduling rules. For researchers concerned with the design of near-optimal solution policies for Multi-armed Restless Bandit Problems, it deploys a systematic procedure to do so based on recent extensions of indexation theory. 180 pp. Englisch. Bookseller Inventory # 9783659283307

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Book Description LAP Lambert Academic Publishing Nov 2012, 2012. Taschenbuch. Book Condition: Neu. Neuware - Mathematical models for the optimal allocation of resources over time under uncertainty are notoriously difficult to solve, both for computational and technical reasons. This book addresses two complex stochastic and dynamic optimization problems, with application in modern sensor systems: (i) hunting multiple elusive targets and (ii) tracking multiple moving targets. These problems are formulated as Multi-armed Restless Bandit models with continuous state, which augments the technical difficulties to derive and compute its optimal solution and motivates the focus on designing tractable and well-performing heuristic policies of priority-index type instead. The book presents how to derive such policies based on a Lagrangian relaxation and decomposition approach. The contents of this book are of relevance both for applied and methodological communities. For modern sensor systems' operators, it provides nearly-optimal and tractable novel scheduling rules. For researchers concerned with the design of near-optimal solution policies for Multi-armed Restless Bandit Problems, it deploys a systematic procedure to do so based on recent extensions of indexation theory. 180 pp. Englisch. Bookseller Inventory # 9783659283307

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Book Description LAP Lambert Academic Publishing Nov 2012, 2012. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand Neuware - Mathematical models for the optimal allocation of resources over time under uncertainty are notoriously difficult to solve, both for computational and technical reasons. This book addresses two complex stochastic and dynamic optimization problems, with application in modern sensor systems: (i) hunting multiple elusive targets and (ii) tracking multiple moving targets. These problems are formulated as Multi-armed Restless Bandit models with continuous state, which augments the technical difficulties to derive and compute its optimal solution and motivates the focus on designing tractable and well-performing heuristic policies of priority-index type instead. The book presents how to derive such policies based on a Lagrangian relaxation and decomposition approach. The contents of this book are of relevance both for applied and methodological communities. For modern sensor systems' operators, it provides nearly-optimal and tractable novel scheduling rules. For researchers concerned with the design of near-optimal solution policies for Multi-armed Restless Bandit Problems, it deploys a systematic procedure to do so based on recent extensions of indexation theory. 180 pp. Englisch. Bookseller Inventory # 9783659283307

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Book Description LAP Lambert Academic Publishing. Paperback. Book Condition: New. Paperback. 180 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.Mathematical models for the optimal allocation of resources over time under uncertainty are notoriously difficult to solve, both for computational and technical reasons. This book addresses two complex stochastic and dynamic optimization problems, with application in modern sensor systems: (i) hunting multiple elusive targets and (ii) tracking multiple moving targets. These problems are formulated as Multi-armed Restless Bandit models with continuous state, which augments the technical difficulties to derive and compute its optimal solution and motivates the focus on designing tractable and well-performing heuristic policies of priority-index type instead. The book presents how to derive such policies based on a Lagrangian relaxation and decomposition approach. The contents of this book are of relevance both for applied and methodological communities. For modern sensor systems operators, it provides nearly-optimal and tractable novel scheduling rules. For researchers concerned with the design of near-optimal solution policies for Multi-armed Restless Bandit Problems, it deploys a systematic procedure to do so based on recent extensions of indexation theory. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Bookseller Inventory # 9783659283307

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Book Description LAP Lambert Academic Publishing, United States, 2012. Paperback. Book Condition: New. Language: English . Brand New Book ***** Print on Demand *****.Mathematical models for the optimal allocation of resources over time under uncertainty are notoriously difficult to solve, both for computational and technical reasons. This book addresses two complex stochastic and dynamic optimization problems, with application in modern sensor systems: (i) hunting multiple elusive targets and (ii) tracking multiple moving targets. These problems are formulated as Multi-armed Restless Bandit models with continuous state, which augments the technical difficulties to derive and compute its optimal solution and motivates the focus on designing tractable and well-performing heuristic policies of priority-index type instead. The book presents how to derive such policies based on a Lagrangian relaxation and decomposition approach. The contents of this book are of relevance both for applied and methodological communities. For modern sensor systems operators, it provides nearly-optimal and tractable novel scheduling rules. For researchers concerned with the design of near-optimal solution policies for Multi-armed Restless Bandit Problems, it deploys a systematic procedure to do so based on recent extensions of indexation theory. Bookseller Inventory # AAV9783659283307

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