Distributed Event Localization via Alternating Direction Method of Multipliers

Zhang, Chunlei; Wang, Yongqiang

doi:10.1109/tmc.2017.2711026

Cited by 26 publications

(16 citation statements)

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“…An exception is the approach of Zhang et al [79], which is similar to our approach in spirit, albeit based on ADMM, in that they allow for the subproblems to be solved inexactly. However, this work focuses on L2-regularized problems and a few selected loss functions, and offers no complexity results.…”

Section: Distributed Batch Solversmentioning

confidence: 99%

Distributed optimization with arbitrary local solvers

Konečný

Jäggi

et al. 2017

Optimization Methods and Software

172

160

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With the growth of data and necessity for distributed optimization methods, solvers that work well on a single machine must be re-designed to leverage distributed computation. Recent work in this area has been limited by focusing heavily on developing highly specific methods for the distributed environment. These special-purpose methods are often unable to fully leverage the competitive performance of their well-tuned and customized single machine counterparts. Further, they are unable to easily integrate improvements that continue to be made to single machine methods. To this end, we present a framework for distributed optimization that both allows the flexibility of arbitrary solvers to be used on each (single) machine locally and yet maintains competitive performance against other state-of-the-art special-purpose distributed methods. We give strong primal-dual convergence rate guarantees for our framework that hold for arbitrary local solvers. We demonstrate the impact of local solver selection both theoretically and in an extensive experimental comparison. Finally, we provide thorough implementation details for our framework, highlighting areas for practical performance gains.Keywords: primal-dual algorithm; distributed computing; machine learning; convergence analysis 2010 Mathematics Subject Classification: 68W15; 68W20; 68W10; 68W40 MotivationRegression and classification techniques, represented in the general class of regularized loss minimization problems [71], are among the most central tools in modern big data analysis, machine learning, and signal processing. For these tasks, much effort from both industry and academia has gone into the development of highly tuned and customized solvers. However, with the massive growth of available datasets, major roadblocks still persist in the distributed setting, where data no longer fit in the memory of a single computer, and computation must be split across multiple machines in a network [3,7,12,18,22,29,32,34,37,46,52,62,64,67,78].On typical real-world systems, communicating data between machines is several orders of magnitude slower than reading data from main memory, e.g. when leveraging commodity hardware. Therefore when trying to translate existing highly tuned single machine solvers to the *Corresponding author. Email: takac.mt@gmail.com 814C. Ma et al. distributed setting, great care must be taken to avoid this significant communication bottleneck [26,74].While several distributed solvers for the problems of interest have been recently developed, they are often unable to fully leverage the competitive performance of their tuned and customized single machine counterparts, which have already received much more research attention. More importantly, it is unfortunate that distributed solvers cannot automatically benefit from improvements made to the single machine solvers, and therefore are forced to lag behind the most recent developments.In this paper, we make a step towards resolving these issues by proposing a general communication-efficient distribu...

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Section: Distributed Batch Solversmentioning

confidence: 99%

Distributed optimization with arbitrary local solvers

Konečný

Jäggi

et al. 2017

Optimization Methods and Software

172

160

View full text Add to dashboard Cite

show abstract

“…In wireless localization problems, the impact of anchor node placement always should be considered. Of course, the geometric layout of anchors and the target has a significant impact on the localization accuracy for the "convex hull" effect of anchors [30]- [32]. It is also easy to imagine that if anchors are deployed close to each other, then the localization problem will be ill-conditioned so that it is more sensitive to the inaccuracy and noise.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Received Signal Strength Based Wireless Source Localization with Inaccurate Anchor Position

Liu¹,

Han²,

Wang³

et al. 2019

Preprint

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Received signal strength(RSS)-based approach of wireless localization is easy to implement at low cost. In practice, exact positions of anchors may not be available. This paper focuses on determining the location of the source in the presence of inexact position of anchors based on RSS directly. This study at first uses Taylor expansion and a min-max approach to get the approximate maximum likelihood estimator of the source coordinates. Then this paper proposes a relaxed semi-definite programming model to circumvent the non-convexity. This paper also proposes a rounding algorithm concerning both the inexact source location and the inaccurate anchor location. Experimental results together with analysis are presented to validate the proposed method

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“…The works [18,7] extended the single-machine trust region Newton method (TRON) [14] for logistic regression in [6] to distributed environments. Experiments in [18] show that TRON is faster than another distributed optimization method ADMM [17,1] when both are implemented in MPI.…”

Section: Related Workmentioning

confidence: 99%

Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization

Lee

Wang

Chen³

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Distributed optimization has become an important research topic for dealing with extremely large volume of data available in the Internet companies nowadays. Additional machines make computation less expensive, but inter-machine communication becomes prominent in the optimization process, and efficient optimization methods should reduce the amount of the communication in order to achieve shorter overall running time. In this work, we utilize the advantages of the recently proposed, theoretically fast-convergent common-directions method, but tackle its main drawback of excessive spatial and computational costs to propose a limited-memory algorithm. The result is an efficient, linear-convergent optimization method for parallel/distributed optimization. We further discuss how our method can exploit the problem structure to efficiently train regularized empirical risk minimization (ERM) models. Experimental results show that our method outperforms state-of-theart distributed optimization methods for ERM problems.

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Distributed Event Localization via Alternating Direction Method of Multipliers

Cited by 26 publications

References 50 publications

Distributed optimization with arbitrary local solvers

Distributed optimization with arbitrary local solvers

Received Signal Strength Based Wireless Source Localization with Inaccurate Anchor Position

Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization

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