Proceedings ArticleDOI
Performance of diffusion adaptation for collaborative optimization
Jianshu Chen,Ali H. Sayed +1 more
- pp 3753-3756
TLDR
An adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes in order to solve the desired optimization problem is derived.Abstract:
We derive an adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes. The cost function is assumed to consist of the sum of individual components, and diffusion adaptation is used to enable the nodes to cooperate locally through in-network processing in order to solve the desired optimization problem. We analyze the mean-square-error performance of the algorithm, including its transient and steady-state behavior. We illustrate one application in the context of least-mean-squares estimation for sparse vectors.read more
Citations
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Journal ArticleDOI
Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks
Jianshu Chen,Ali H. Sayed +1 more
TL;DR: An adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes, which endow networks with adaptation abilities that enable the individual nodes to continue learning even when the cost function changes with time.
Proceedings ArticleDOI
Distributed optimization via diffusion adaptation
TL;DR: An iterative diffusion mechanism to optimize a global cost function in a distributed manner over a network of nodes and allows the nodes to cooperate and diffuse information in real-time is developed.
Proceedings ArticleDOI
Combination weights for diffusion strategies with imperfect information exchange
Xiaochuan Zhao,Ali H. Sayed +1 more
TL;DR: This paper investigates the mean-square performance of adaptive diffusion algorithms in the presence of various sources of imperfect information exchanges and quantization errors, and reveals that link noise over the regression data modifies the dynamics of the network evolution, and leads to biased estimates in steady-state.
Proceedings ArticleDOI
Diffusion gradient temporal difference for cooperative reinforcement learning with linear function approximation
TL;DR: This work introduces a diffusion-based algorithm in which multiple agents cooperate to predict a common and global state-value function by sharing local estimates and local gradient information among neighbors, to make it applicable to multiagent settings.
Proceedings ArticleDOI
Distributed throughput optimization over P2P mesh networks using diffusion adaptation
TL;DR: This work develops a decentralized adaptive strategy for throughput maximization over peer-to-peer (P2P) networks that can cope with changing network topologies, is robust to network disruptions, and does not rely on central processors.
References
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Book
An introduction to optimization
TL;DR: This review discusses mathematics, linear programming, and set--Constrained and Unconstrained Optimization, as well as methods of Proof and Some Notation, and problems with Equality Constraints.
Book
Adaptive Filters
TL;DR: Adaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors.
Journal ArticleDOI
Diffusion LMS Strategies for Distributed Estimation
TL;DR: This work motivates and proposes new versions of the diffusion LMS algorithm that outperform previous solutions, and provides performance and convergence analysis of the proposed algorithms, together with simulation results comparing with existing techniques.
Journal ArticleDOI
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
Cassio G. Lopes,Ali H. Sayed +1 more
TL;DR: Closed-form expressions that describe the network performance in terms of mean-square error quantities are derived and the resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment.