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Submodularity in dynamics and contro...

Clark, Andrew.

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Submodularity in dynamics and control of networked systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Submodularity in dynamics and control of networked systems/ by Andrew Clark ... [et al.].
其他作者:	Clark, Andrew.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xvii, 210 p. :ill., digital ;24 cm.
內容註:	Part I: Submodular Functions and Optimization -- Submodular Functions and Matroids -- Centralized Submodular Maximization -- Distributed Submodular Maximization -- Submodularity in Dynamics and Control -- Background on Control of Networked Systems -- Submodular Optimization for Smooth Convergence in Networked Systems -- Selecting Catalyst Nodes for Synchronization -- Input Selection for Robustness to Noise -- Input Node Selection under Noise Injection Attacks -- Input Node Selection for Joint Performance and Controllability.
Contained By:	Springer eBooks
標題:	Submodular functions. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-26977-1
ISBN:	9783319269771$q(electronic bk.)

Submodularity in dynamics and control of networked systems
Submodularity in dynamics and control of networked systems[electronic resource] /by Andrew Clark ... [et al.]. - Cham :Springer International Publishing :2016. - xvii, 210 p. :ill., digital ;24 cm. - Communications and control engineering,0178-5354. - Communications and control engineering..

Part I: Submodular Functions and Optimization -- Submodular Functions and Matroids -- Centralized Submodular Maximization -- Distributed Submodular Maximization -- Submodularity in Dynamics and Control -- Background on Control of Networked Systems -- Submodular Optimization for Smooth Convergence in Networked Systems -- Selecting Catalyst Nodes for Synchronization -- Input Selection for Robustness to Noise -- Input Node Selection under Noise Injection Attacks -- Input Node Selection for Joint Performance and Controllability.

This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllability with provable optimality bounds in static as well as time-varying networks. Throughout the text, the submodular framework is illustrated with the help of numerical examples and application-based case studies in biological, energy and vehicular systems. The book effectively combines two areas of growing interest, and will be especially useful for researchers in control theory, applied mathematics, networking or machine learning with experience in submodular optimization but who are less familiar with the problems and tools available for networked systems (or vice versa) It will also benefit graduate students, offering consistent terminology and notation that greatly reduces the initial effort associated with beginning a course of study in a new area.

ISBN: 9783319269771$q(electronic bk.)

Standard No.: 10.1007/978-3-319-26977-1doiSubjects--Topical Terms:

726577
Submodular functions.

LC Class. No.: QA166.6

Dewey Class. No.: 511.6

Submodularity in dynamics and control of networked systems
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245 0 0 $a Submodularity in dynamics and control of networked systems $h [electronic resource] / $c by Andrew Clark ... [et al.].
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490 1 $a Communications and control engineering, $x 0178-5354
505 0 $a Part I: Submodular Functions and Optimization -- Submodular Functions and Matroids -- Centralized Submodular Maximization -- Distributed Submodular Maximization -- Submodularity in Dynamics and Control -- Background on Control of Networked Systems -- Submodular Optimization for Smooth Convergence in Networked Systems -- Selecting Catalyst Nodes for Synchronization -- Input Selection for Robustness to Noise -- Input Node Selection under Noise Injection Attacks -- Input Node Selection for Joint Performance and Controllability.
520 $a This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllability with provable optimality bounds in static as well as time-varying networks. Throughout the text, the submodular framework is illustrated with the help of numerical examples and application-based case studies in biological, energy and vehicular systems. The book effectively combines two areas of growing interest, and will be especially useful for researchers in control theory, applied mathematics, networking or machine learning with experience in submodular optimization but who are less familiar with the problems and tools available for networked systems (or vice versa) It will also benefit graduate students, offering consistent terminology and notation that greatly reduces the initial effort associated with beginning a course of study in a new area.
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