ARock: An Algorithmic Framework for Asynchronous Parallel Coordinate Updates

Peng, Zhimin; Xu, Yangyang; Yan, Ming; Yin, Wotao

doi:10.1137/15m1024950

Cited by 213 publications

(311 citation statements)

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“…Third, it is interesting to develop a primal-dual type MC-BCD, which would apply to a model-free DMDP along a single trajectory. Yet another line of work applies block coordinate update to linear and nonlinear fixed-point problems [18,17,5] because it can solve optimization problems in imaging and conic programming, which are equipped with nonsmooth, nonseparable objectives, and constraints.…”

Section: Possible Future Workmentioning

confidence: 99%

Markov chain block coordinate descent

Sun

et al. 2019

Comput Optim Appl

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The method of block coordinate gradient descent (BCD) has been a powerful method for largescale optimization. This paper considers the BCD method that successively updates a series of blocks selected according to a Markov chain. This kind of block selection is neither i.i.d. random nor cyclic. On the other hand, it is a natural choice for some applications in distributed optimization and Markov decision process, where i.i.d. random and cyclic selections are either infeasible or very expensive. By applying mixing-time properties of a Markov chain, we prove convergence of Markov chain BCD for minimizing Lipschitz differentiable functions, which can be nonconvex. When the functions are convex and strongly convex, we establish both sublinear and linear convergence rates, respectively. We also present a method of Markov chain inertial BCD. Finally, we discuss potential applications.KEYWORDS block coordinate gradient descent, Markov chain, Markov chain Monte Carlo, Markov decision process, decentralized optimization AMS Primary: 90C26; secondary: 90C40, 68W154 Extension to nonsmooth problems 13 5 Empirical Markov chain dual coordinate ascent 16 *

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Section: Possible Future Workmentioning

confidence: 99%

Markov chain block coordinate descent

Sun

et al. 2019

Comput Optim Appl

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“…Compared to the optimization algorithms used in these methods (Haufe et al, 2008; Chang et al, 2010; Sohrabpour et al, 2016), the proposed algorithm in this paper is more efficient and robust, and it is also able to tackle large-scale problems. Further, with this type of problem formation, it is possible to adopt some computing techniques (Peng et al, 2015) to further accelerate the algorithm, which will be the future work.…”

Section: Discussionmentioning

confidence: 99%

s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography

et al. 2016

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EEG source imaging enables us to reconstruct current density in the brain from the electrical measurements with excellent temporal resolution (~ ms). The corresponding EEG inverse problem is an ill-posed one that has infinitely many solutions. This is due to the fact that the number of EEG sensors is usually much smaller than that of the potential dipole locations, as well as noise contamination in the recorded signals. To obtain a unique solution, regularizations can be incorporated to impose additional constraints on the solution. An appropriate choice of regularization is critically important for the reconstruction accuracy of a brain image. In this paper, we propose a novel Sparsity and SMOOthness enhanced brain TomograpHy (s-SMOOTH) method to improve the reconstruction accuracy by integrating two recently proposed regularization techniques: Total Generalized Variation (TGV) regularization and ℓ1−2 regularization. TGV is able to preserve the source edge and recover the spatial distribution of the source intensity with high accuracy. Compared to the relevant total variation (TV) regularization, TGV enhances the smoothness of the image and reduces staircasing artifacts. The traditional TGV defined on a 2D image has been widely used in the image processing field. In order to handle 3D EEG source images, we propose a voxel-based Total Generalized Variation (vTGV) regularization that extends the definition of second-order TGV from 2D planar images to 3D irregular surfaces such as cortex surface. In addition, the ℓ1−2 regularization is utilized to promote sparsity on the current density itself. We demonstrate that ℓ1−2 regularization is able to enhance sparsity and accelerate computations than ℓ1 regularization. The proposed model is solved by an efficient and robust algorithm based on the difference of convex functions algorithm (DCA) and the alternating direction method of multipliers (ADMM). Numerical experiments using synthetic data demonstrate the advantages of the proposed method over other state-of-the-art methods in terms of total reconstruction accuracy, localization accuracy and focalization degree. The application to the source localization of event-related potential data further demonstrates the performance of the proposed method in real-world scenarios.

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“…Challenges of distributed learning also lie in asynchrony and delay introduced by e.g., IoT mobility and heterogeneity. Asynchronous parallel learning schemes are thus worth investigating by leveraging advances in static optimization settings [14], [75]. From distributed machine learning to distributed control, multi-agent reinforcement learning will play a critical role in distributed control for IoT [58].…”

Section: Lessons Learned and The Road Aheadmentioning

confidence: 99%

Learning and Management for Internet of Things: Accounting for Adaptivity and Scalability

Chen

Barbarossa

Wang³

et al. 2019

Proc. IEEE

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Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on. automation, and thus intelligence toward the vision of realtime IoT. However, despite the popularity of IoT, several critical challenges must be addressed before embracing its full potential [5], [86]. To this end, we highlight three key challenges that are arguably expected to be at the epicenter of emerging IoT research fields. Fig. 1:Internet of Everything[3]. Extremeheterogeneity. The computational and communication capacities of connected devices differ due to differences in hardware (e.g., CPU frequency), communication protocol (e.g., ZigBee, WiFi), and energy availability (e.g., battery level) [103]. The tasks carried out on various devices are often considerably diverse, e.g., motion sensors monitor human behavior in a smart home [60], while cameras are responsible for recognizing a suspicious behavior in a crowded environment, or, vehicle plates in a parking garage.Unpredictable dynamics. Unlike many existing communication, computing and networking platforms, the IoT dynamics can stem from multiple sources, where adaptivity is not only critical but also essential in designing hardware and management protocols. Such sources entail human-in-the-loop dynamics in addition to physical objects [60], demand response in energy systems [40], and intelligent automotive applications [59]. In these applications, IoT dynamics are intertwined with or even partially determined by human behavior [34], [69], [73] -as such, high degree of adaptivity in the algorithm and hardware design is needed.Scalability at the core. IoT entails an intelligent network infrastructure with...

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ARock: An Algorithmic Framework for Asynchronous Parallel Coordinate Updates

Cited by 213 publications

References 54 publications

Markov chain block coordinate descent

Markov chain block coordinate descent

s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography

Learning and Management for Internet of Things: Accounting for Adaptivity and Scalability

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