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distributed and Localized Covariance Control of Coupled Systems: A System Level Approach

IEEE CONTROL SYSTEMS LETTERS 2025年 9卷 180-185页

作者： Khalil, Ahmed Lee, Yoonjae Bakolas, Efstathios Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

This work is concerned with finite-horizon covariance steering for networked, discrete-time stochastic linear systems with dynamically coupled subsystems, extending beyond existing work limited to decoupled 'agents.' We formulate a localized covariance-steering problem and, using the system & acirc;& Abreve;& Sacute;level synthesis (SLS) framework, recast it as a convex optimization over system responses. After analyzing separability and applying a transformation for nonseparable objectives, we derive a distributed consensus-ADMM algorithm that respects each subsystem's local information and communication constraints. We validate our approach on a power network with 36 interconnected subsystems.

关键词： Noise Convex functions Power system dynamics Covariance matrices Vectors Uncertainty Power grids Mathematical models History distributed algorithms distributed control networked control systems stochastic optimal control

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distributed K-Clustering with Exponential Convergence

Distributed K-Clustering with Exponential Convergence

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American Control Conference (ACC)

作者： Wang, L. Tron, R. Univ Calif Irvine Samueli Sch Engn Irvine CA 92697 USA Boston Univ Coll Engn Boston MA 02215 USA

ISBN: (数字)9781665451963

ISBN: (纸本)9781665451963

We consider the problem of distributed K-clustering in a network of N agents where each agent holds a part of a dataset. The goal for the agents is to agree, in a distributed manner, on a set of K cluster centers that effectively summarize the entire dataset (e.g., by identifying points where the data is most concentrated), without explicitly exchanging all the data between all the agents or with a central node. First, we consider the special case when there is only one cluster (K = 1). We propose the distributed 1-clustering Multiplier Algorithm (Di 1MA), which ensures exponential convergence of all the copies of the cluster center at each agent to a common value by combining the properties of consensus algorithms and of the augmented Lagrangian method. Second, based on the previous case, we propose an algorithm called distributed K-clustering Multiplier Algorithm (DiKMA) to solve the case with K > 1 cluster centers. For this case, using ideas from switching algorithms, our solution alternates q steps of updates of the cluster centers with one update of the local assignments of data to cluster centers. We show that there must exist a q large enough such that the algorithm ensures the exponential convergence of the K cluster centers. We provide simulations to verify the proposed algorithms, and illustrate some of their properties.

关键词： Clustering algorithms distributed databases Switches Consensus algorithm Approximation algorithms Trajectory distributed algorithms

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Optimal Communication Bounds for Classic Functions in the Coordinator Model and Beyond 2024

Optimal Communication Bounds for Classic Functions in the Co...

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56th Annual ACM Symposium on Theory of Computing (STOC)

作者： Esfandiari, Hossein Kacham, Praneeth Mirrokni, Vahab Woodruff, David P. Zhong, Peilin Google London England Carnegie Mellon Univ Pittsburgh PA USA Google Res New York NY USA

ISBN: (纸本)9798400703836

In the coordinator model of communication with B servers, given an arbitrary non-negative function f, we study the problem of approximating the sum Sigma i([n]) f(x(i)) up to a 1 +/- epsilon factor. Here the vector x is an element of R-n is defined to be x = x ( 1)+ center dot center dot center dot+x(s), where x(j) >= 0 denotes the non-negative vector held by the 9 -th server. A special case of the problem is when f(x) = x(k) which corresponds to the well-studied problem of F-k moment estimation in the distributed communication model. We introduce a new parameter c(f)[s] which captures the communication complexity of approximating Sigma(i is an element of[n]) f(x(i)) and for a broad class of functions f which includes f(x) = x(k) for k >= 2 and other robust functions such as the Huber loss function, we give a two round protocol that uses total communication c(f)[s]/epsilon(2) bits, up to polylogarithmic factors. For this broad class of functions, our result improves upon the communication bounds achieved by Kannan, Vempala, andWoodruff (COLT 2014) andWoodruff and Zhang (STOC 2012), obtaining the optimal communication up to polylogarithmic factors in the minimum number of rounds. We show that our protocol can also be used for approximating higher-order correlations. Our results are part of a broad framework for optimally sampling from a joint distribution in terms of the marginal distributions held on individual servers. Apart from the coordinator model, algorithms for other graph topologies in which each node is a server have been extensively studied. We argue that directly lifting protocols from the coordinator model to other graph topologies will require some nodes in the graph to send a lot of communication. Hence, a natural question is the type of problems that can be efficiently solved in general graph topologies. We address this question by giving communication efficient protocols in the so-called personalized CONGEST model for solving linear regression and lo

关键词： distributed algorithms coordinator model moment estimation personalized CONGEST model

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Fast distributed Beamforming without Receiver Feedback 56

Fast Distributed Beamforming without Receiver Feedback

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56th Asilomar Conference on Signals, Systems, and Computers

作者： Chakrabarti, Kushal Bedi, Amrit S. Dagefu, Fikadu T. Twigg, Jeffrey N. Chopra, Nikhil Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA DEVCOM Army Res Lab Adelphi MD USA Univ Maryland Dept Mech Engn College Pk MD 20742 USA

ISBN: (纸本)9781665459068

distributed multi-agent beampattern matching inherently enables covert communication by constructively combining the transmitted signals at target receivers and forming nulls towards the adversaries. Most existing beamforming methodologies achieve this by relying on partial or full-state real-time feedback from the receivers. This paper proposes a novel distributed beamforming technique that does not assume any feedback from the receivers or channel parameters, such as multipath fading. The proposed algorithm works in a server-agent architecture of the beamforming agents, eliminating the need for receivers' feedback. Our algorithm is built on the classical gradient-descent (GD) method. However, when the problem is ill-conditioned, GD requires many iterations to converge and is unstable against system noise. We propose an iterative preconditioning technique to mitigate the deleterious effects of the data points' conditioning on the convergence rate, facilitating a rapid establishment of communication links with far-field targets. The empirical results demonstrate the proposed beamforming algorithm's favorable convergence rate and robustness against unknown multipath fading in realistic environments.

关键词： Antenna arrays Cooperative communication distributed algorithms MIMO communication

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Similarity Joins of Sparse Features

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ACM International Conference on Management of Data (SIGMOD)

作者： Metwally, Ahmed Shum, Michael Uber Technol Inc Sunnyvale CA 94158 USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9798400704222

Identifying all pairs of records from two datasets whose similarity exceeds a given threshold is crucial for data cleaning and clustering. Our work on similarity-joins is motivated by detecting fraud and abuse. We focus on similarity-joins of sparse features, where records represent sparse sets, multisets, or vectors. Most state-of-the-art techniques are distributed versions of sequential algorithms. This is the reason they fail to scale when the alphabet is large, the records are large, or some elements are shared by numerous records. In this paper, we propose FastScalableSparseJoiner (FSSJ) that introduces quasi-prefix filtering, a novel flavor of prefix filtering [7] that exploits the skew in element popularity to avoid processing the most-popular elements without broadcasting the sorted elements to all executors. FastScalableSparseJoiner effectively prunes candidate pairs, and efficiently distributes the computation of partial results for load-balancing across executors. FSSJ can be adopted in any shared-nothing architecture. Based on our evaluation on synthetic and real datasets using a Spark-based implementation, FSSJ is very competitive on small datasets, and is the only algorithm that can join industry-scale datasets even with limited resources.

关键词： Similarity Joins distributed algorithms Load balancing

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Black Hole Search in Dynamic Cactus Graph 18th

Black Hole Search in Dynamic Cactus Graph

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18th International Conference and Workshops on algorithms and Computation (WALCOM)

作者： Bhattacharya, Adri Italiano, Giuseppe F. Mandal, Partha Sarathi Indian Inst Technol Guwahati Gauhati India

ISBN: (纸本)9789819705658;9789819705665

We study the problem of black hole search by a set of mobile agents, where the underlying graph is a dynamic cactus. A black hole is a dangerous vertex in the graph that eliminates any visiting agent without leaving any trace behind. Key parameters that dictate the complexity of finding the black hole include: the number of agents required (termed as size), the number of moves performed by the agents in order to determine the black hole location (termed as move) and the time (or round) taken to terminate. This problem has already been studied where the underlying graph is a dynamic ring [6]. In this paper, we extend the same problem to a dynamic cactus. We introduce two categories of dynamicity, but still, the underlying graph needs to be connected: first, we examine the scenario where, at most, one dynamic edge can disappear or reappear at any round. Secondly, we consider the problem for at most k dynamic edges. In both scenarios, we establish lower and upper bounds for the necessary number of agents, moves and rounds.

关键词： Black hole search Dynamic cactus graph Dynamic networks Time-varying graphs Mobile Agents distributed algorithms

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A distributed Algorithm for Solving Linear Algebraic Equations Over Random Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第5期66卷 2399-2406页

作者： Alaviani, Seyyed Shaho Elia, Nicola Iowa State Univ Ames IA 50011 USA Clemson Univ Dept Mech Engn Clemson SC 29634 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

This article considers the problem of solving linear algebraic equations of the form Ax=b among multiagents, which seek a solution by using local information in presence of random communication topologies. The equation is solved by m agents where each agent only knows a subset of rows of the partitioned matrix [A,b]. The problem is formulated such that this formulation does not need the distribution of random interconnection graphs. Therefore, this framework includes asynchronous updates and/or unreliable communication protocols. The random Krasnoselskii-Mann iterative algorithm is applied that converges almost surely and in mean square to a solution of the problem for any matrices A and b and any initial conditions of agents' states. The algorithm is a totally asynchronous algorithm without requiring a priori B-connectivity and distribution dependency assumptions. The algorithm is able to solve the problem even if the weighted matrix of the graph is periodic and irreducible for synchronous protocol. It is demonstrated that the limit point to which the agents' states converge is determined by the unique solution of a convex optimization problem regardless of the distribution of random communication graphs. Finally, some numerical examples are given to show the results.

关键词： Mathematical model Protocols Hilbert space distributed algorithms Convex functions Network topology Topology Asynchronous distributed algorithm linear algebraic equations random graphs

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distributed Dynamic Quantile Solver With Plug-and-Play Operation

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IEEE ACCESS 2021年 9卷 165517-165525页

作者： Seong, Jeong Mo Kim, Jeong Woo Lee, Seungjoon Shim, Hyungbo Seoul Natl Univ Dept Elect & Comp Engn ASRI Seoul 08826 South Korea NAVER LABS Seongnam 13638 South Korea

In this paper, we propose a continuous-time distributed algorithm for the dynamic quantile problem. The problem is to find the quantile of time-varying signals in a network of agents, each of which having the signal of its own. For example, this problem includes finding the median, maximum, or the second largest value of the signals. The proposed algorithm guarantees convergence from arbitrary initial conditions and does not use the decaying gains. Hence our algorithm is suitable for plug-and-play operation, where agents may freely join or leave the network during the operation. An application to a simplified electricity market problem is presented to show the effectiveness of the design.

关键词： Heuristic algorithms distributed algorithms Optimization Multi-agent systems Couplings Measurement Convergence Blended dynamics consensus protocols continuous time systems distributed algorithms multi-agent systems

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distributed Stochastic Optimization in Networks With Low Informational Exchange

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第5期67卷 2989-3008页

作者： Li, Wenjie Assaad, Mohamad CentraleSupelec Lab Signaux & Syst F-91190 Gif Sur Yvette France Univ Paris Saclay Lab Signaux & Syst L2S CentraleSupelec IIMR CNRS 8506 F-91190 Gif Sur Yvette France

We consider a distributed stochastic optimization problem in networks with finite number of nodes. Each node adjusts its action to optimize the global utility of the network, which is defined as the sum of local utilities of all nodes. While Gradient descent method is a common technique to solve such optimization problem, the computation of the gradient may require much information exchange. In this paper, we consider that each node can only have a noisy numerical observation of its local utility, of which the closed-form expression is not available. This assumption is quite realistic, especially when the system is either too complex or constantly changing. Nodes may exchange partially the observation of their local utilities to estimate the global utility at each timeslot. We propose a distributed algorithm based on stochastic perturbation, under the assumption that each node has only part of the local utilities of the other nodes. We use stochastic approximation tools to prove that our algorithm converges almost surely to the optimum, given that the objective function is smooth and strictly concave. The convergence rate is also derived, under the additional assumption of strongly concave objective function. It is shown that the convergence rate scales as O (K-0.5) after a sufficient number of iterations K > K-0, which is the optimal rate order in terms of K for our problem. Although the proposed algorithm can be applied to general optimization problems, we perform simulations for a typical power control problem in wireless networks and present numerical results to corroborate our claims.

关键词： Optimization derivative-free optimization stochastic approximation convergence analysis distributed algorithms

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distributed Constrained Optimization Over Unbalanced Directed Networks Using Asynchronous Broadcast-Based Algorithm

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第3期66卷 1102-1115页

作者： Li, Huaqing Lu, Qingguo Chen, Guo Huang, Tingwen Dong, Zhaoyang Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Texas A&M Univ Qatar Sci Program Doha 23874 Qatar

This article focuses on distributed convex optimization problems over an unbalanced directed multiagent (no central coordinator) network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known convex cost functions. Every single agent in the network only knows its local objective function and local inequality constraint, and is constrained to a privately known convex set. Furthermore, we particularly discuss the scenario in which the interactions among agents over the whole network are subjected to possible link failures. To collaboratively solve the optimization problem, we mainly concentrate on an epigraph form of the original constrained optimization to overcome the unbalancedness of directed networks, and propose a new distributed asynchronous broadcast-based optimization algorithm. The algorithm allows that not only the updates of agents are asynchronous in a distributed fashion, but also the step-sizes of all agents are uncoordinated. An important characteristic of the proposed algorithm is to cope with the constrained optimization problem in the case of unbalanced directed networks whose communications are subjected to possible link failures. Under two standard assumptions that the communication network is directly strongly connected and the subgradients of all local objective functions are bounded, we provide an explicit analysis for convergence of the algorithm. Simulation results obtained by three numerical experiments substantiate the feasibility of the algorithm and validate the theoretical findings.

关键词： Signal processing algorithms Convex functions Linear programming Convergence distributed algorithms Cost function Asynchronous algorithm distributed convex optimization epigraph form inequality constraints unbalanced directed networks

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