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Cooperative Data-Driven Distributionally Robust Optimization

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第10期65卷 4400-4407页

作者： Cherukuri, Ashish Cortes, Jorge Univ Groningen ENTEG NL-9712 CP Groningen Netherlands Univ Calif San Diego Dept Mech & Aerosp Engn San Diego CA 92093 USA

We study a class of multiagent stochastic optimization problems where the objective is to minimize the expected value of a function which depends on a random variable. The probability distribution of the random variable is unknown to the agents. The agents aim to cooperatively find, using their collected data, a solution with guaranteed out-of-sample performance. The approach is to formulate a data-driven distributionally robust optimization problem using Wasserstein ambiguity sets, which turns out to be equivalent to a convex program. We reformulate the latter as a distributed optimization problem and identify a convex-concave augmented Lagrangian, whose saddle points are in correspondence with the optimizers, provided a min-max interchangeability criteria is met. Our distributed algorithm design, then consists of the saddle-point dynamics associated to the augmented Lagrangian. We formally establish that the trajectories converge asymptotically to a saddle point and, hence, an optimizer of the problem. Finally, we identify classes of functions that meet the min-max interchangeability criteria.

关键词： Optimization Random variables Probability distribution Measurement distributed algorithms Linear programming Convex functions Data-driven methods distributed optimization distributionally robust optimization multiagent systems

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Multi-Agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第2期5卷 3221-3226页

作者： Semnani, Samaneh Hosseini Liu, Hugh Everett, Michael de Ruiter, Anton How, Jonathan P. Ryerson Univ Aerosp Engn Dept Toronto ON M5B 2K3 Canada Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Univ Toronto Fac Appl Sci & Engn Toronto ON M5S 1A1 Canada MIT Aerosp Controls Lab 77 Massachusetts Ave Cambridge MA 02139 USA

This letter introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments. Individually, RL and FMP algorithms each have their own limitations. FMP is not able to produce time-optimal paths and existing RL solutions are not able to produce collision-free paths in dense environments. Therefore, we first tried improving the performance of recent RL approaches by introducing a new reward function that not only eliminates the requirement of a pre supervised learning (SL) step but also decreases the chance of collision in crowded environments. That improved things, but there were still a lot of failure cases. So, we developed a hybrid approach to leverage the simpler FMP approach in stuck, simple and high-risk cases, and continue using RL for normal cases in which FMP can't produce optimal path. Also, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show that the proposed algorithm outperforms both deep RL and FMP algorithms and produces up to 50$\%$ more successful scenarios than deep RL and up to 75$\%$ less extra time to reach goal than FMP.

关键词： Motion planning distributed algorithms collision avoidance deep learning reinforcement learning trajectory optimization hybrid control

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High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 2128-2142页

作者： Du, Yuqing Yang, Sheng Huang, Kaibin Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Paris Saclay Cent Supelec Lab Signals & Syst Gif Sur Yvette 91190 France

Edge machine learning involves the deployment of learning algorithms at the wireless network edge so as to leverage massive mobile data for enabling intelligent applications. The mainstream edge learning approach, federated learning, has been developed based on distributed gradient descent. Based on the approach, stochastic gradients are computed at edge devices and then transmitted to an edge server for updating a global AI model. Since each stochastic gradient is typically high-dimensional, communication overhead becomes a bottleneck for edge learning. To address this issue, we propose a novel framework of hierarchical gradient quantization and study its effect on the learning performance. First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional Grassmannian codebook, respectively. Subsequently, the quantized normalized block gradients are scaled and cascaded to yield the quantized normalized stochastic gradient using a socalled hinge vector, which is compressed using another low-dimensional Grassmannian quantizer designed under the criterion of minimum distortion. The other feature of the framework is a bit-allocation scheme for reducing the distortion, which divides the total quantization bits to determine the resolutions of low-dimensional quantizers. The framework is proved to guarantee model convergency by analyzing the convergence rate as a function of quantization bits. Furthermore, by simulation, our design is shown to substantially reduce the communication overhead compared with the state-of-the-art signSGD scheme, while achieving similar learning accuracies.

关键词： Vector quantization gradient methods learning (artificial intelligence) distributed algorithms

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Network flows that solve least squares for linear equations

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AUTOMATICA 2020年 120卷

作者： Liu, Yang Lou, Youcheng Anderson, Brian D. O. Shi, Guodong Australian Natl Univ Res Sch Elect Energy & Mat Engn Canberra ACT 0200 Australia Chinese Acad Sci Acad Math & Syst Sci MDIS Beijing 100190 Peoples R China Hangzhou Dianzi Univ Hangzhou 310018 Peoples R China Data61 CSIRO Canberra ACT 0200 Australia Univ Sydney Australian Ctr Field Robot Sydney NSW 2006 Australia

This paper presents a first-order distributed continuous-time algorithm for computing the least-squares solution to a linear equation over networks. Given the uniqueness of the solution, with nonintegrable and diminishing step size, convergence results are provided for fixed graphs. The exact rate of convergence is also established for various types of step size choices falling into that category. For the case where non-unique solutions exist, convergence to one such solution is proved for constantly connected switching graphs with square integrable step size. Validation of the results and illustration of the impact of step size on the convergence speed are made using a few numerical examples. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： distributed algorithms Linear equation Least-squares solutions

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Periodic event-triggered control of flywheel energy storage matrix systems for wind farms

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IET CONTROL THEORY AND APPLICATIONS 2020年第11期14卷 1467-1477页

作者： Sun, Yizhuo Hu, Jiangping Liu, Jianxing Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

Flywheel energy storage plays a significant role in improving the reliability and efficiency of wind farm operations, in recent years. In order to reduce the communication burden, this study proposes a distributed algorithm based on the event triggered to coordinate the operation of wind farm and flywheel energy storage matrix system (FESMS). Different from the traditional sampling mechanism, a periodic sampling-based event-triggered condition, which is based on the disagreement vector and the measurement error, is proposed for the FESMS dispatch problem. Furthermore, a self-triggered method is also studied as well considering the periodic sampling case. The algorithm achieves ratio consensus in a balanced directed graph topology. By simulation, the effectiveness of the proposed algorithm is verified.

关键词： flywheels distributed control directed graphs graph theory wind power plants energy storage topology distributed algorithms wind farm operations distributed algorithm traditional sampling mechanism periodic sampling-based event-triggered condition self-triggered method periodic sampling case periodic event-triggered control flywheel energy storage matrix systems reliability

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AsySPA: An Exact Asynchronous Algorithm for Convex Optimization Over Digraphs

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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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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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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 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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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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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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