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AsySPA: An Exact Asynchronous Algorithm for Convex Optimization Over Digraphs

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第6期65卷 2494-2509页

作者： Zhang, Jiaqi You, Keyou Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

This paper proposes a novel exact asynchronous subgradient-push algorithm (AsySPA) to solve an additive cost optimization problem over digraphs where each node only has access to a local convex function and updates asynchronously with an arbitrary rate. Specifically, each node of a strongly connected digraph does not wait for updates from other nodes but simply starts a new update within any bounded time interval by using local information available from its in-neighbors. "Exact" means that every node of the AsySPA can asymptotically converge to the same optimal solution, even under different update rates among nodes and bounded communication delays. To address uneven update rates, we design a simple mechanism to adaptively adjust stepsizes per update in each node, which is substantially different from the existing works. Then, we construct a delay-free augmented system to address asynchrony and delays, and study its convergence by proposing a generalized subgradient algorithm, which clearly has its own significance and helps us to explicitly evaluate the convergence rate of the AsySPA. Finally, we demonstrate advantages of the AsySPA in both theory and simulation.

关键词： Delays Convergence Optimization Synchronization Convex functions Clocks distributed algorithms Asynchronous subgradient-push algorithm (AsySPA) distributed optimization problem (DOP) even update rates (EUR) uneven update rates (UUR)

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Leader-Follower Network Aggregative Game With Stochastic Agents' Communication and Activeness

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第12期65卷 5496-5502页

作者： Shokri, Mohammad Kebriaei, Hamed Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran 131451384 Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran 193955746 Iran

This article presents a leader-follower scheme for network aggregative games. The followers and leader are selfish cost minimizing agents. The cost function of each follower is affected by its own strategy, the strategy of leader and the aggregate strategy of its neighbors through a communication graph. Also, the leader's cost function depends on its own strategy and the aggregate strategy of all the followers. The leader infinitely often wakes up, receives the aggregate strategy of the followers, updates its decision value, and broadcasts it to all the followers. Then, the followers apply the updated strategy of the leader into their cost functions. The establishment of information exchange between each neighboring pair of followers, and also, decision updating by a follower at each iteration, that is to say the activeness of the follower in that iteration, are both considered to be drawn from two arbitrary distributions. Moreover, a distributed algorithm based on subgradient method is proposed for updating the strategies of leader and followers. The convergence of the proposed algorithm to the unique Stackelberg equilibrium point of the game is proven in both almost sure and mean square senses.

关键词： Cost function Games distributed algorithms Aggregates Artificial neural networks Convergence Stochastic processes distributed algorithm leader– follower network aggregative game (NAG) stochastic network subgradient method

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Segmentation of large-scale remotely sensed images on a Spark platform: A strategy for handling massive image tiles with the MapReduce model

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年第0期162卷 137-147页

作者： Wang, Ning Chen, Fang Yu, Bo Qin, Yuchu Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 Dengshuang South Rd Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Hainan Key Lab Earth Observat Sanya 572029 Peoples R China

Image segmentation is essential in object-based image analysis. Numerous image segmentation algorithms have been proposed and widely applied to process remote sensing images, but most of them are designed to deal with single scenes. As the volume of images grows rapidly, handling images with single machines is becoming increasingly difficult, and the size of a composite image can be larger than the CPU memory of a single computer. To address this problem, a distributed image segmentation strategy is proposed in this paper. The two main steps of the proposed strategy are as follows. First, a prepared massive image is loaded and then decomposed into sub-images that are distributed across multiple computers;algorithms are then used in parallel to segment each sub-image into a large number of initial objects. Secondly, the proposed object resegmentation method is applied to the initial boundary objects in each sub-image in order to merge these objects. The sub-images are then ingested from the different computers in order to obtain the final segmentation image. Two classical segmentation algorithms are employed to test the proposed strategy in eight different study areas that include urban area, suburban zone and agricultural landscape. Both the intersection over union and the F-measure metrics show that the proposed strategy can help to solve the problem of the data volume being too large to fit on a single machine, and that it also performs better than comparative strategies. The proposed strategy not only has the ability to segment very large images, but also accelerates the segmentation and segmentation-based applications so that they can match the image acquisition rate.

关键词： Segmentation algorithms distributed algorithms Image processing GeoTrellis Spark

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Asynchronous Approximation of a Single Component of the Solution to a Linear System

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第3期7卷 975-986页

作者： Ozdaglar, Asuman Shah, Devavrat Yu, Christina Lee MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA

We present a distributed asynchronous algorithm for approximating a single component of the solution to a system of linear equations Ax = b, where A is a positive definite real matrix and b is an element of R-n. This can equivalently be formulated as solving for x(i) in x = Gx + z for some G and z such that the spectral radius of G is less than 1. Our algorithm relies on the Neumann series characterization of the component x(i), and is based on residual updates. We analyze our algorithm within the context of a cloud computation model motivated by frameworks, such as Apache Spark, in which the computation is split into small update tasks performed by small processors with shared access to a distributed file system. We prove a robust asymptotic convergence result when the spectral radius rho(vertical bar G vertical bar) < 1, regardless of the precise order and frequency in which the update tasks are performed. We provide convergence rate bounds that depend on the order of update tasks performed, analyzing both deterministic update rules via counting weighted random walks, as well as probabilistic update rules via concentration bounds. The probabilistic analysis requires analyzing the product of random matrices that are drawn from distributions that are time and path dependent. We specifically consider the setting where n is large, yet G is sparse, e.g., each row has at most d nonzero entries. This is motivated by applications in which G is derived from the edge structure of an underlying graph. Our results prove that if the local neighborhood of the graph does not grow too quickly as a function of n, our algorithm can provide significant reduction in computation cost as opposed to any algorithm that computes the global solution vector x. Our algorithm obtains an epsilon parallel to x parallel to(2) additive approximation for x(i) in constant time with respect to the size of the matrix when the maximum row sparsity d = O(1) and 1/(1 = parallel to G parallel to(2)) = O(

关键词： Task analysis Approximation algorithms Mathematical model Convergence Computational modeling Symmetric matrices Jacobian matrices Linear system of equations local computation asynchronous randomized algorithms distributed algorithms

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Zonally Robust Decentralized Optimization for Global Energy Interconnection: Case Study on Northeast Asian Countries

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2020年第4期17卷 2120-2129页

作者： Ding, Tao Yang, Qingrun Wen, Ya Ning, Ye Yang, Yongheng Blaabjerg, Frede Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Peoples R China Global Energy Interconnect Res Inst Beijing 100072 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Nowadays, the entire world is facing challenges in energy and environment. To resolve these problems, the power systems are interconnected to promote the development of renewable energy sources (RESs). However, the economic dispatch (ED) problem for the global energy interconnection (GEI) should tackle two issues: 1) handle the uncertainty from RES and allocate the responsibility among the interconnected countries and 2) protect the information privacy through the dispatch. Motivated by the above, this article proposes a zonally adjustable robust decentralized ED model for the GEI. In the model, each country is only responsible for its own uncertainty, and tie-line power flows remain unchanged under uncertainties. Moreover, an alternating direction method of multipliers (ADMM)-based fully distributed algorithm is used, in which only limited information should be exchanged between neighboring countries. Finally, a case study on the Northeast Asian countries verifies the effectiveness of the proposed method. Note to Practitioners-Since the renewable energy generation has a spatial correlation among regional countries, global energy interconnection (GEI) aims to combine several power systems together to promote the renewable energy accommodation. However, two problems need to be considered: 1) Information Privacy: The information privacy of the power system in each country should be preserved, which prevents the GEI from conducting a centralized optimal dispatch framework and 2) Uncertainty: The uncertain output of renewable energy resources brings challenge to the power system secure operation. The main contribution of this article is to set up a zonally robust decentralized optimization for the GEI, where the zonally robust economic dispatch (ED) is conducted by the area control error (ACE) system to manage the difference between scheduled and actual generation under the uncertainties, and the alternating direction method of multipliers (ADMMs) algorithm is adopted f

关键词： Robustness Uncertainty Optimization Economics Power systems distributed algorithms Privacy Decentralized algorithm economic dispatch (ED) global energy interconnection (GEI) zonal robustness

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Brief announcement: Efficient load-balancing through distributed token dropping 34

Brief announcement: Efficient load-balancing through distrib...

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34th International Symposium on distributed Computing, DISC 2020

作者： Brandt, Sebastian Keller, Barbara Rybicki, Joel Suomela, Jukka Uitto, Jara ETH Zürich Switzerland Aalto University Finland IST Austria Klosterneuburg Austria

ISBN: (纸本)9783959771689

We introduce a new graph problem, the token dropping game, and we show how to solve it efficiently in a distributed setting. We use the token dropping game as a tool to design an efficient distributed algorithm for the stable orientation problem, which is a special case of the more general locally optimal semi-matching problem. The prior work by Czygrinow et al. (DISC 2012) finds a locally optimal semi-matching in O(∆5) rounds in graphs of maximum degree ∆, which directly implies an algorithm with the same runtime for stable orientations. We improve the runtime to O(∆4) for stable orientations and prove a lower bound of Ω(∆) rounds. © Sebastian Brandt, Barbara Keller, Joel Rybicki, Jukka Suomela, and Jara Uitto;licensed under Creative Commons License CC-BY 34th International Symposium on distributed Computing (DISC 2020).

关键词： distributed algorithms Graph problems Semi-matching

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distributed Generalized Nash Equilibrium Seeking of N-Coalition Games with Inequality Constraints

Distributed Generalized Nash Equilibrium Seeking of N-Coalit...

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IEEE Conference on Decision and Control

作者： Chao Sun Guoqiang Hu School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665436601

In this paper, we study the generalized Nash equilibrium (GNE) seeking problem for an N-coalition game with inequality constraints. First, using full decision information, a finite-time average consensus-based approach is proposed. It is demonstrated that the algorithm converges to a GNE (specifically, a variational equilibrium) of the game. Then, using the finite-time consensus tracking technique, we propose a distributed algorithm that only requires neighboring action information for each agent. The solution by using partial decision information covers the distributed GNE seeking solutions of generalized non-cooperative games as a special case.

关键词： Conferences Games Nash equilibrium distributed algorithms Convergence

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distributed alternated-inertia generalized Nash equilibrium seeking algorithm: The partial-decision-information case

Distributed alternated-inertia generalized Nash equilibrium ...

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International Conference on Information Science and Technology (ICIST)

作者： Zhangcheng Feng Wenying Xu Jinde Cao Shaofu Yang School of Mathematics Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

This paper investigates the distributed computation issue of generalized Nash equilibrium (GNE) in games with globally shared constraints under partial-decision information, where players only get information from their neighbors rather than all the other players. First, we propose two kinds of fully distributed projected-gradient algorithms with inertia/alternated inertia respectively, to seek the GNE. Then, by using the operator splitting technique, the convergence of the proposed algorithms can be guaranteed under the assumptions of Lipschitz continuity and strong monotonicity of the pseudo-gradient mappings. Finally, several numerical examples are provided to verify the developed algorithms.

关键词： Couplings Information science Computational modeling Games Nash equilibrium distributed algorithms Convergence

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Generating Massive Scale-free Networks: Novel Parallel algorithms using the Preferential Attachment Model

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ACM TRANSACTIONS ON PARALLEL COMPUTING 2020年第2期7卷 1–35页

作者： Alam, Maksudul Khan, Maleq Perumalla, Kalyan S. Marathe, Madhav Oak Ridge Natl Lab 1 Bethel Valley Rd Oak Ridge TN 37831 USA Texas A&M Univ Kingsville 700 Univ Blvd Kingsville TX 78363 USA Univ Virginia Network Syst Sci & Adv Comp Div Biocomplex Inst & Initiat Res Pk Blvd Charlottesville VA 22904 USA Univ Virginia Dept Comp Sci Res Pk Blvd Charlottesville VA 22904 USA

Recently, there has been substantial interest in the study of various random networks as mathematical models of complex systems. As real-life complex systems grow larger, the ability to generate progressively large random networks becomes all the more important. This motivates the need for efficient parallel algorithms for generating such networks. Naive parallelization of sequential algorithms for generating random networks is inefficient due to inherent dependencies among the edges and the possibility of creating duplicate (parallel) edges. In this article, we present message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. Our algorithms are experimentally verified to scale very well to a large number of processing elements (PEs), providing near-linear speedups. The algorithms have been exercised with regard to scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.

关键词： Network science random networks preferential attachment distributed algorithms

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distributed optimal scheduling of integrated energy system based on ADMM algorithm

Distributed optimal scheduling of integrated energy system b...

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Sustainable Power and Energy Conference (iSPEC)

作者： Yi Wu Liangzhong Yao Siyang Liao Beilin Mao Institute of Electrical and Automation Wuhan University Wuhan China

ISBN: (纸本)9781665414401

With the change of energy consumption structure, the integrated energy system coupled with multiple energy flows such as electricity, natural gas and heat is developing rapidly. At present, the decision-making center of the integrated energy park is the park dispatching control center. The dispatching process involves in the collection of distributed resource data, the joint centralized scheduling of multiple data, and the allocation and distribution of dispatching tasks. However, with the expansion of the scale of the integrated energy system, the centralized control center needs to collect and process a large amount of data, this depends on the strong computing power of the park control center and the reliable communication ability with distributed resources. For the future large-scale comprehensive energy park, centralized computing has many deficiencies. In this paper, the distributed computing method is used to decompose the joint scheduling problem into electric-gas subproblems, and the strong coupling relationship between electricity, natural gas and heat is considered; In the longitudinal consideration of source and reservoir interaction, a day ahead scheduling model considering economic cost and fluctuation cost of tie line is established. Finally, an example is given to analyze the feasibility of the distributed optimization model.

关键词： Costs Processor scheduling Computational modeling distributed databases Optimal scheduling Dispatching distributed algorithms

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