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10 - Cooperative distributed multi-agent optimization
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- By Angelia Nedić, University of Illinois at Urbana-Champaign, Asuman Ozdaglar, Massachusetts Institute of Technology
- Edited by Daniel P. Palomar, Hong Kong University of Science and Technology, Yonina C. Eldar, Weizmann Institute of Science, Israel
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- Book:
- Convex Optimization in Signal Processing and Communications
- Published online:
- 23 February 2011
- Print publication:
- 03 December 2009, pp 340-386
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- Chapter
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Summary
This chapter presents distributed algorithms for cooperative optimization among multiple agents connected through a network. The goal is to optimize a global-objective function which is a combination of local-objective functions known by the agents only. We focus on two related approaches for the design of distributed algorithms for this problem. The first approach relies on using Lagrangian-decomposition and dual-subgradient methods. We show that this methodology leads to distributed algorithms for optimization problems with special structure. The second approach involves combining consensus algorithms with subgradient methods. In both approaches, our focus is on providing convergence-rate analysis for the generated solutions that highlight the dependence on problem parameters.
Introduction and motivation
There has been much recent interest in distributed control and coordination of networks consisting of multiple agents, where the goal is to collectively optimize a global objective. This is motivated mainly by the emergence of large-scale networks and new networking applications such as mobile ad hoc networks and wireless-sensor networks, characterized by the lack of centralized access to information and time-varying connectivity. Control and optimization algorithms deployed in such networks should be completely distributed, relying only on local observations and information, robust against unexpected changes in topology, such as link or node failures, and scalable in the size of the network.
This chapter studies the problem of distributed optimization and control of multiagent networked systems. More formally, we consider a multiagent network model, where m agents exchange information over a connected network.
22 - Incentives and Pricing in Communications Networks
- from IV - Additional Topics
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- By Asuman Ozdaglar, Department of Electrical Engineering and Computer Science, MIT, R. Srikant, Department of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign
- Edited by Noam Nisan, Hebrew University of Jerusalem, Tim Roughgarden, Stanford University, California, Eva Tardos, Cornell University, New York, Vijay V. Vazirani, Georgia Institute of Technology
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- Book:
- Algorithmic Game Theory
- Published online:
- 31 January 2011
- Print publication:
- 24 September 2007, pp 571-592
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Summary
Abstract
In this chapter, we study two types of pricing mechanisms: one where the goal of the pricing scheme is to achieve some socially beneficial objective for the network and the other where prices are set by multiple competing service providers to maximize their revenues. For both cases, we present an overview of the mathematical models involved and the relevant optimization and game-theoretic techniques needed to study these models. We study the impact of different degrees of strategic interactions among users and between users and service providers on the network performance. We also relate our models and solutions to practical resource allocation mechanisms used in communication networks such as congestion control, routing, and scheduling. We conclude the chapter with a brief introduction to other game-theoretic topics in emerging networks.
This chapter studies the problem of decentralized resource allocation among competing users in communication networks. The growth in the scale of communication networks and the newly emerging interactions between administrative domains and end users with different needs and quality of service requirements necessitate new approaches to the modeling and control of communication networks that recognize the difficulty of formulating and implementing centralized control protocols for resource allocation. The current research in this area has developed a range of such approaches. Central to most of these approaches is the modeling of end users and sometimes also of service providers as self-interested agents that make decentralized and selfish decisions.