Handbook of Optimization in Complex Networks

Springer Optimization and Its Applications

Book 57
Springer Science & Business Media
1
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Complex Social Networks is a newly emerging (hot) topic with applications in a variety of domains, such as communication networks, engineering networks, social networks, and biological networks. In the last decade, there has been an explosive growth of research on complex real-world networks, a theme that is becoming pervasive in many disciplines, ranging from mathematics and computer science to the social and biological sciences. Optimization of complex communication networks requires a deep understanding of the interplay between the dynamics of the physical network and the information dynamics within the network. Although there are a few books addressing social networks or complex networks, none of them has specially focused on the optimization perspective of studying these networks. This book provides the basic theory of complex networks with several new mathematical approaches and optimization techniques to design and analyze dynamic complex networks. A wide range of applications and optimization problems derived from research areas such as cellular and molecular chemistry, operations research, brain physiology, epidemiology, and ecology.
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About the author

Panos Pardalos is Distinguished Professor of Industrial and Systems Engineering at the University of Florida. He is also affiliated faculty member of the Computer Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the director of the Center for Applied Optimization. He is the editor-in-chief of the Journal of Global Optimization, Journal of Optimization Letters, and Computational Management Science. In addition, he is the managing editor of several book series, and a member of the editorial board of several international journals. He is the author of 8 books and the editor of several books. He has written numerous articles and developed several well known software packages. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

My T. Thai is an Associate Professor in the Computer & Information Science & Engineering department at the University of Florida. Her research interests are centered on the Combinatorial Optimization and its connection to Networks, including communication networks, online social networks, wireless sensor networks, and biological networks. The results of her work have been published in about 60 articles and 4 books since 2005. She has engaged in many professional activities, serving many conferences such as being a conference chair of COCOON 2010 and DIS 2011. She is an associate editor of Journal of Combinatorial Optimization (JOCO), Optimization Letters, and Ad Hoc & Wireless Sensor Networks journal. She is also a guest editor of several other journals and a series editor of Springer Briefs in Optimization. She has received many research awards including a Provost's Excellence Award for Assistant Professors at the University of Florida, a DoD Young Investigator Award, and an NSF CAREER Award.

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Additional Information

Publisher
Springer Science & Business Media
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Published on
Jan 28, 2012
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Pages
546
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ISBN
9781461407546
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Language
English
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Genres
Computers / Networking / Hardware
Mathematics / Applied
Mathematics / General
Mathematics / Optimization
Technology & Engineering / Telecommunications
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Content Protection
This content is DRM protected.
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This book is intended to be used as a textbook for graduate students studying theoretical computer science. It can also be used as a reference book for researchers in the area of design and analysis of approximation algorithms. Design and Analysis of Approximation Algorithms is a graduate course in theoretical computer science taught widely in the universities, both in the United States and abroad. There are, however, very few textbooks available for this course. Among those available in the market, most books follow a problem-oriented format; that is, they collected many important combinatorial optimization problems and their approximation algorithms, and organized them based on the types, or applications, of problems, such as geometric-type problems, algebraic-type problems, etc. Such arrangement of materials is perhaps convenient for a researcher to look for the problems and algorithms related to his/her work, but is difficult for a student to capture the ideas underlying the various algorithms. In the new book proposed here, we follow a more structured, technique-oriented presentation. We organize approximation algorithms into different chapters, based on the design techniques for the algorithms, so that the reader can study approximation algorithms of the same nature together. It helps the reader to better understand the design and analysis techniques for approximation algorithms, and also helps the teacher to present the ideas and techniques of approximation algorithms in a more unified way.
Congestion Control in Data Transmission Networks details the modeling and control of data traffic in communication networks. It shows how various networking phenomena can be represented in a consistent mathematical framework suitable for rigorous formal analysis. The monograph differentiates between fluid-flow continuous-time traffic models, discrete-time processes with constant sampling rates, and sampled-data systems with variable discretization periods.
The authors address a number of difficult real-life problems, such as:
optimal control of flows with disparate, time-varying delay;
the existence of source and channel nonlinearities;
the balancing of quality of service and fairness requirements; and
the incorporation of variable rate allocation policies.
Appropriate control mechanisms which can handle congestion and guarantee high throughput in various traffic scenarios (with different networking phenomena being considered) are proposed. Systematic design procedures using sound control-theoretic foundations are adopted. Since robustness issues are of major concern in providing efficient data-flow regulation in today’s networks, sliding-mode control is selected as the principal technique to be applied in creating the control solutions. The controller derivation is given extensive analytical treatment and is supported with numerous realistic simulations. A comparison with existing solutions is also provided. The concepts applied are discussed in a number of illustrative examples, and supported by many figures, tables, and graphs walking the reader through the ideas and introducing their relevance in real networks.
Academic researchers and graduate students working in computer networks and telecommunications and in control (especially time-delay systems and discrete-time optimal and sliding-mode control) will find this text a valuable assistance in ensuring smooth data-flow within communications networks.
This book surveys state-of-the-art optimization modeling for design, analysis, and management of wireless networks, such as cellular and wireless local area networks (LANs), and the services they deliver. The past two decades have seen a tremendous growth in the deployment and use of wireless networks. The current-generation wireless systems can provide mobile users with high-speed data services at rates substantially higher than those of the previous generation. As a result, the demand for mobile information services with high reliability, fast response times, and ubiquitous connectivity continues to increase rapidly. The optimization of system performance has become critically important both in terms of practical utility and commercial viability, and presents a rich area for research.

In the editors' previous work on traditional wired networks, we have observed that designing low cost, survivable telecommunication networks involves extremely complicated processes. Commercial products available to help with this task typically have been based on simulation and/or proprietary heuristics. As demonstrated in this book, however, mathematical programming deserves a prominent place in the designer's toolkit. Convenient modeling languages and powerful optimization solvers have greatly facilitated the implementation of mathematical programming theory into the practice of commercial network design.

These points are equally relevant and applicable in today’s world of wireless network technology and design. But there are new issues as well: many wireless network design decisions, such as routing and facility/element location, must be dealt with in innovative ways that are unique and distinct from wired (fiber optic) networks. The book specifically treats the recent research and the use of modeling languages and network optimization techniques that are playing particularly important and distinctive roles in the wireless domain.

There has been much recent progress in approximation algorithms for nonconvex continuous and discrete problems from both a theoretical and a practical perspective. In discrete (or combinatorial) optimization many approaches have been developed recently that link the discrete universe to the continuous universe through geomet ric, analytic, and algebraic techniques. Such techniques include global optimization formulations, semidefinite programming, and spectral theory. As a result new ap proximate algorithms have been discovered and many new computational approaches have been developed. Similarly, for many continuous nonconvex optimization prob lems, new approximate algorithms have been developed based on semidefinite pro gramming and new randomization techniques. On the other hand, computational complexity, originating from the interactions between computer science and numeri cal optimization, is one of the major theories that have revolutionized the approach to solving optimization problems and to analyzing their intrinsic difficulty. The main focus of complexity is the study of whether existing algorithms are efficient for the solution of problems, and which problems are likely to be tractable. The quest for developing efficient algorithms leads also to elegant general approaches for solving optimization problems, and reveals surprising connections among problems and their solutions. A conference on Approximation and Complexity in Numerical Optimization: Con tinuous and Discrete Problems was held during February 28 to March 2, 1999 at the Center for Applied Optimization of the University of Florida.
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