Control Techniques for Complex Networks
- Author: Sean Meyn, University of Florida
- Date Published: January 2008
- availability: Available
- format: Hardback
- isbn: 9780521884419
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Power grids, flexible manufacturing, cellular communications: interconnectedness has consequences. This remarkable book gives the tools and philosophy you need to build network models detailed enough to capture essential dynamics but simple enough to expose the structure of effective control solutions. Core chapters assume only exposure to stochastic processes and linear algebra at undergraduate level; later chapters are for advanced graduate students and researchers/practitioners. This gradual development bridges classical theory with the state-of-the-art. The workload model at the heart of traditional analysis of the single queue becomes a foundation for workload relaxations used in the treatment of complex networks. Lyapunov functions and dynamic programming equations lead to the celebrated MaxWeight policy along with many generalizations. Other topics include methods for synthesizing hedging and safety stocks, stability theory for networks, and techniques for accelerated simulation. Examples and figures throughout make ideas concrete. Solutions to end-of-chapter exercises are available on a companion website.
Read more- Control perspective supports development of simplified analysis for robust control solutions
- Emphasis on visualization techniques builds intuition and understanding
- Range of exercises enables practice with simple concepts through to exploration of theory
Reviews & endorsements
'Sean Meyn's text is a wonderful piece of work … It progresses through a series of important topics, running the gamut from modern control techniques for queuing system analysis, to optimization of deterministic network models, to computer simulation methods; and all the while, it provides rigorous mathematical foundations alongside a variety of clever, practical applications. The lively writing style and apt examples keep everything interesting, and I believe that readers will greatly appreciate and benefit from this unique book.' David M. Goldsman, Georgia Institute of Technology
See more reviews'Sean Meyn's earlier book with Tweedie is the bible for economists who use Markov models to do everything from formulating asset pricing models to constructing Bayesian posteriors for dynamic models. This book is a gold mine of useful new ideas. I predict that the ideas in chapter 11 alone will have a big impact on the way we think about computing rational expectations equilibria.' Thomas Sargent, New York University; Winner of the 2011 Nobel Prize in Economic Sciences
'The first comprehensive account of some major strands of research in modeling, approximation, stability analysis and optimization of stochastic networks, from a leader in the field … Notable among these are its coverage of deterministic fluid limits, controlled random walk models, approximation via workload relaxation, and implications of these to stability and optimization of networks. Several important special instances are worked out in detail. A valuable resource for both researchers and practitioners.' Vivek S. Borkar, Tata Institute of Fundamental Research
'In my opinion this book is written primarily for seasoned researchers in the field who need a nice source of existing results and ideas. In this vein the book is outstanding and it should become an indispensable aid to researchers and practitioners. … All in all this is an excellent book …' Mathematical Reviews
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×Product details
- Date Published: January 2008
- format: Hardback
- isbn: 9780521884419
- length: 582 pages
- dimensions: 260 x 183 x 33 mm
- weight: 1.166kg
- contains: 98 exercises
- availability: Available
Table of Contents
Preface
1. Introduction
Part I. Modeling and Control:
2. Examples
3. The single-server queue
4. Scheduling
Part II. Workload:
5. Workload and scheduling
6. Routing and resource pooling
7. Demand
Part III. Stability and Performance:
8. Foster-Lyapunov techniques
9. Optimization
10. ODE methods
11. Simulation and learning
Appendix. Markov models
References
Index.-
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Control Techniques for Complex Networks
Sean Meyn
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