Adaptive Penalty-Based Distributed Stochastic Convex Optimization

Towfic, Zaid J.; Sayed, Ali H.

doi:10.1109/tsp.2014.2331615

Cited by 95 publications

(82 citation statements)

References 43 publications

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“…Since p j,t (t) is a feasible point satisfying Cp j,t (t) = 1 m and ∑ n j=1 y j,i = 1, (42) is equivalent to Cp i,t+1 (t) = 1 m , which indicates that the new iterator p i,t+1 (t) satisfies the equality constraints of the model (17).…”

Section: Lemma 3 (Conditions On Gradient Projection Matrix For Iteratmentioning

confidence: 99%

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An Adaptive Fusion Strategy for Distributed Information Estimation Over Cooperative Multi-Agent Networks

Zhou

Sheng

2017

IEEE Trans. Inform. Theory

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Article (Accepted Version) http://sro.sussex.ac.uk Tian, Daxin, Zhou, Jianshan and Sheng, Zhengguo (2017) An adaptive fusion strategy for distributed information estimation over cooperative multi-agent networks. IEEE Transactions on Information Theory, 63 (5). pp. [3076][3077][3078][3079][3080][3081][3082][3083][3084][3085][3086][3087][3088][3089][3090][3091] This version is available from Sussex Research Online: http://sro.sussex.ac.uk/66726/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Abstract-In this paper, we study the problem of distributed information estimation that is closely relevant to some networkbased applications such as distributed surveillance, cooperative localization and optimization. We consider a problem where an application area containing multiple information sources of interest is divided into a series of subregions in which only one information source exists. The information is presented as a signal variable which has finite states associated with certain probabilities. The probability distribution of information states of all the subregions constitutes a global information picture for the whole area. Agents with limited measurement and communication ranges are assumed to monitor the area, and cooperatively create a local estimate of the global information. To efficiently approximate the actual global information using individual agents' own estimates, we propose an adaptive distributed information fusion strategy and use it to enhance the local Bayesian rule based updating procedure. Specifically, this adaptive fusion strategy is induced by iteratively minimizing a Jensen-Shannon divergence based objective function. A constrained optimization model is also presented to derive minimum Jensen-Shannon divergence weights at each agent for fusing local neighbors' individual estimates. Theoretical analysis and numerical results are supplemented to show the convergence performance and effectiveness of the proposed solution.

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Section: Lemma 3 (Conditions On Gradient Projection Matrix For Iteratmentioning

confidence: 99%

“…At this point, the new iterator p j,t+1 (t) also satisfies the inequality constraints of the optimization model (17).…”

Section: Lemma 3 (Conditions On Gradient Projection Matrix For Iteratmentioning

confidence: 99%

An Adaptive Fusion Strategy for Distributed Information Estimation Over Cooperative Multi-Agent Networks

Zhou

Sheng

2017

IEEE Trans. Inform. Theory

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“…Further distributed algorithm for set constrained optimization was investigated in Bianchi and Jakubowicz [34] and Lou et al [35]. To work out the distributed optimization problems with 2 Complexity asynchronous step-sizes or inequality-equality constraints, distributed Lagrangian and penalty primal-dual subgradient algorithms were developed in Zhu and Martinez [36] and Towfic and Sayed [37]. Both of them were designed for function constrained problems.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs

Lü

2017

Complexity

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This paper focuses on a class of event-triggered discrete-time distributed consensus optimization algorithms, with a set of agents whose communication topology is depicted by a sequence of time-varying networks. The communication process is steered by independent trigger conditions observed by agents and is decentralized and just rests with each agent's own state. At each time, each agent only has access to its privately local Lipschitz convex objective function. At the next time step, every agent updates its state by applying its own objective function and the information sent from its neighboring agents. Under the assumption that the network topology is uniformly strongly connected and weight-balanced, the novel event-triggered distributed subgradient algorithm is capable of steering the whole network of agents asymptotically converging to an optimal solution of the convex optimization problem. Finally, a simulation example is given to validate effectiveness of the introduced algorithm and demonstrate feasibility of the theoretical analysis.

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“…Apart from [4], this algorithm has effectively been used for distributed Pareto optimization in various scenarios and applications such as in the works of [19]- [23].…”

Section: Introductionmentioning

confidence: 99%

On the asymptotic bias of the diffusion-based distributed pareto optimization

et al. 2017

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Abstract:We revisit the asymptotic bias analysis of the distributed Pareto optimization algorithm developed based on the diffusion strategies. We propose an alternative way to analyze the asymptotic bias of this algorithm at small step-sizes and show that the asymptotic bias descends to zero with a linear dependence on the largest step-size parameter when this parameter is sufficiently small. In addition, through the proposed analytic approach, we provide an expression for the small-step-size asymptotic bias when a condition assumed jointly on the combination matrices and the step-sizes does not strictly hold. This is a likely scenario in practice, which has not been considered in the original paper that introduced the algorithm. Our methodology provides new insights into the inner workings of the diffusion Pareto optimization algorithm while being considerably less involved than the small-step-size asymptotic bias analysis presented in the original work. This is because we take advantage of the special eigenstructure of the composite combination matrix used in the algorithm without calling for any eigenspace decomposition or matrix inversion.

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Adaptive Penalty-Based Distributed Stochastic Convex Optimization

Cited by 95 publications

References 43 publications

An Adaptive Fusion Strategy for Distributed Information Estimation Over Cooperative Multi-Agent Networks

An Adaptive Fusion Strategy for Distributed Information Estimation Over Cooperative Multi-Agent Networks

Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs

On the asymptotic bias of the diffusion-based distributed pareto optimization

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