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Maximum independent set formation on a finite grid by myopic robots

THEORETICAL COMPUTER SCIENCE 2025年 1031卷

作者： Das, Raja Sharma, Avisek Sau, Buddhadeb Jadavpur Univ Dept Math Kolkata India

This work deals with the Maximum Independent Set (MAXIS) formation problem in a finite rectangular grid by autonomous robots. Suppose we are given a set of identical robots, where each robot is placed on a node of a finite rectangular grid G such that no two robots are on the same node. The MAXIS formation problem asks to design an algorithm and each robot will move autonomously after executing the algorithm and terminate at a node such that after a finite time the set of nodes occupied by the robots is a maximum independent set of G. We assume that robots are anonymous and they execute the same distributed algorithm. Previous works solved this problem using one or several door nodes through which the robots enter the grid or the graph one by one and occupy the required nodes. In this work, we propose a deterministic algorithm that solves the MAXIS formation problem in a more generalized scenario, i.e., when the total number of required robots to form a MAXIS are arbitrarily placed on the grid. The proposed algorithm works under a semi-synchronous scheduler using robots with only two hop visibility range and only three colors.

关键词： Myopic robot Maximum independent set Finite grid Autonomous robots Robot with lights distributed algorithms

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A Safe First-Order Method for Pricing-Based Resource Allocation in Safety-Critical Networks

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 521-533页

作者： Turan, Berkay Hutchinson, Spencer Alizadeh, Mahnoosh Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

We introduce a novel algorithm for solving network utility maximization (NUM) problems that arise in resource allocation schemes over networks with known safety-critical constraints, where the constraints form an arbitrary convex and compact feasible set. Inspired by applications where customers' demand can only be affected through posted prices and real-time two-way communication with customers is not available, we require an algorithm to generate "safe prices." This means that at no iteration should the realized demand in response to the posted prices violate the safety constraints of the network. Thus, in contrast to existing distributed first-order methods, our algorithm, called safe pricing for NUM (SPNUM), is guaranteed to produce feasible primal iterates at all iterations. At the heart of the algorithm lie two key steps that must go hand in hand to guarantee safety and convergence: first, applying a projected gradient method on a shrunk feasible set to get the desired demand, and second, estimating the price response function of the users and determining the price so that the induced demand is close to the desired demand. We ensure safety by adjusting the shrinkage to account for the error between the induced demand and the desired demand. In addition, by gradually reducing the amount of shrinkage and the step size of the gradient method, we prove that the primal iterates produced by the SPNUM achieve a sublinear static regret of O ( log (T)) after T time steps.

关键词： Resource management Pricing Gradient methods Optimization Safety Vectors Iterative algorithms Convergence Network systems Convex functions distributed algorithms multi-agent systems optimization methods safety smart devices

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Decentralized management of unbalanced multi-agent distribution systems: A transactive approach leveraging ADMM framework

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IET GENERATION TRANSMISSION & DISTRIBUTION 2025年第1期19卷

作者： Fattaheian-Dehkordi, Sajjad Aghaei, Jamshid Amjady, Nima Aalto Univ Dept Elect Engn & Automat Espoo Finland Cent Queensland Univ Sch Engn & Technol Rockhampton Qld Australia Federat Univ Australia Ctr New Energy Transit Res Ballarat Vic Australia

The emergence of multi-agent systems and significant integration of distributed energy sources (DESs) are transforming distribution networks. This necessitates a decentralized management strategy for unbalanced operation in multi-agent distribution systems (MADSs) due to the autonomous nature and potential for unbalanced injection of single-phase DESs. Accordingly, this paper proposes a novel approach for decentralized management of unbalanced operation in MADSs. The approach leverages a customized alternating direction method of multipliers to facilitate decentralized decision-making, while incorporating transactive energy signals aligned with the alternating direction method of multiplier framework to enable independent agent operation. In this scheme, independent agents would optimize their operating costs considering the announced transactive signals, which model the power prices and power loss in the grid. The decentralized structure enables agents to apply stochastic and condition value at risk methods to address the uncertainty and associated risk in scheduling resources. Furthermore, without violating the privacy concerns of agents, the developed transactive-based scheme facilitates minimizing the asymmetrical condition, caused by the unbalanced integration of DESs, in the power request at the connection point of MADSs and transmission networks. Finally, the proposed methodology is simulated on 37-bus and 123-bus test-systems to study its effectiveness in managing the MADSs with unbalanced integration of DESs.

关键词： distribution networks multi-agent systems renewable energy sources decentralised control distributed algorithms distributed control

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Diffusion Generalized Minimum Total Error Entropy Algorithm

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 751-755页

作者： Cai, Peng Lin, Dongyuan Qian, Junhui Zheng, Yunfei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China

Both the minimum error entropy (MEE) and mixture MEE (MMEE) are extensively employed in distributed adaptive filters, exhibiting their robustness against non-Gaussian noise by capturing high-order statistical information from network data. However, the fixed shape of the Gaussian kernel function existing in MEE and MMEE restricts their flexibility, leading to reduced robustness and deteriorated performance. To address this issue, a novel diffusion generalized minimum total error entropy (DGMTE) algorithm is first proposed in this letter, using a generalized MEE criterion to significantly improve the performance of error-in-variables models-based algorithms under non-Gaussian noise. Moreover, as a special case of DGMTE, a generalized minimum total error entropy (GMTE) algorithm is also proposed, and the local convergence analysis of DGMTE is given. Finally, simulations show the superiorities of DGMTE in comparison with other representative algorithms.

关键词： Noise Signal processing algorithms Entropy Shape Adaptation models Kernel Estimation distributed algorithms Cost function Convergence Adaptive filter distributed estimation errors-in-variables model generalized minimum error entropy total least squares

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Penalty Removal Search Algorithm for distributed Optimization of Nonconvex Functions in Economic Dispatch

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Ge, Yiyang Wang, Zhanshan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China State Key Lab Synthet Automation Proc Ind Shenyang 110819 Peoples R China

Nonconvexity is a usually overlooked factor in economic dispatch (ED). Enhancing the nonconvexity of the objective function leads traditional convex optimization algorithms easily to fall into the local optimum. To address the above problem, a penalty removal search algorithm (PRSA) is proposed for ED nonconvex optimization. It is composed of two distributed optimization algorithms embedded in a reinforcement learning framework. In Phase I of PRSA, a distributed optimization algorithm with projection operators is designed. It uses fewer variables to locate the region where the optimal solution belongs by the cooperative Q-learning. In Phase II of PRSA, the sigmoid function serves as a penalty function to form the second distributed optimization algorithm. This is used to skip the searched solutions and allow the algorithm to continue searching for more feasible solutions. The PRSA solves the problem that the algorithm misses feasible solutions when the nonconvex coefficients increase. Finally, the effectiveness of the PRSA is verified by numerical examples.

关键词： Linear programming Economics distributed algorithms Cost function Q-learning Germanium Costs Valves Fuels Convex functions Collaborative Q-learning (CQL) algorithm distributed optimization economic dispatch (ED) nonconvex optimization penalty removal search algorithm (PRSA)

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distributed Shortest Link Scheduling algorithms With Constant Time Complexity in IoT Under Rayleigh Fading

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IEEE ACCESS 2020年 8卷 103245-103255页

作者： Yu, Kan Yu, Jiguo Dong, Anming Li, Guangshun Qufu Normal Univ Sch Informat Sci & Engn Rizhao 276826 Peoples R China Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Jinan 250353 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan 250353 Peoples R China

For the shortest link scheduling (SLS), i.e., scheduling a given set of links with minimum time slots, we consider the distributed algorithm design by using the locality of the protocol model with high fidelity under the Rayleigh fading. Different from most previous works, focusing on distributed algorithm design under the deterministic SINR model, which ignores the fading effects in signal propagation, we first show that a successful link of protocol model is also feasible under the deterministic SINR model, then it can be scheduled successfully with high probability under the Rayleigh fading, by upper-bounding interference outside interference range of protocol model. Then on the basis of this key conclusion, we design LLS-SLS algorithm to solve SLS within . Specifically, is the number of a sender & x2019;s neighbors within some certain range, and can be upper-bounded. Next, based on the concept of random contention, we design CLLS algorithm to schedule all links after costing time slots. In addition, extensive simulations evaluate the performance of two proposed algorithms.

关键词： Shortest link scheduling Rayleigh fading locality distributed algorithms

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A frame of reference for the performance evaluation of asynchronous, distributed decision-making algorithms

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JOURNAL OF SYSTEMS AND SOFTWARE 2000年第1期55卷 45-56页

作者： Ghosh, S Lee, TS Joo, SS Arizona State Univ Dept Comp Sci & Engn Tempe AZ 85287 USA Vitria Technol Sunnyvale CA 94086 USA Elect & Telecommun Res Inst Taejon 305600 South Korea

The discipline of distributed algorithms aims at distributing a computational task among multiple, concurrent processors to achieve higher throughput, efficiency, performance and other advantages. Although parallel execution time, concurrency, scalability, isoefficiency function, and speed-up have been proposed in the literature as performance metrics, the use of speed-up and its variation, scaled speed-up, dominate the literature. Speed-up is defined as the ratio of the execution time of the best known serial algorithm to the execution time of the parallel algorithm on a given number of concurrent processors. This paper analyzes critically the role of speed-up from the perspective of an emerging class of distributed algorithms, termed asynchronous, distributed, decisionmaking (ADDM) algorithms. ADDM algorithms constitute the underlying control of many real-world systems, wherein the constituent sub-components are geographically dispersed, they interact between themselves asynchronously, and are permitted autonomy in local decision-making. The term real-world implies that such systems are subject to computer control and that they relate to everyday life and are beneficial to the society in the large. Analysis reveals several key limitations of speed-up from the perspective of describing the performance of ADDM algorithms. First, by definition, speed-up utilizes the execution time of a centralized system as the basis for comparison and is, therefore, only a relative measure. The centralized implementation is obviously neither optimal nor the absolute best. Second, for many systems that are amenable to ADDM algorithms, speed-up may not constitute a relevant performance metric. This paper adopts Ferrari's definition of performance and presents a frame of reference for the performance evaluation of ADDM algorithms. While novel decision criterion may need to be developed for each ADDM algorithm, given the diversity inherent in systems, the frame of reference implies that

关键词： absolute performance asynchronous systems distributed algorithms distributed systems ideal results performance criteria absolute standard real-world systems

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Geometric Convergence of Gradient Play algorithms for distributed Nash Equilibrium Seeking

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第11期66卷 5342-5353页

作者： Tatarenko, Tatiana Shi, Wei Nedic, Angelia Arizona State Univ Sch Elect Comp & Energy Engn Phoenix AZ 85004 USA Moscow Inst Phys & Technol Dolgoprudnyi 141701 Russia

We study distributed algorithms for seeking a Nash equilibrium in a class of convex networked Nash games with strongly monotone mappings. Each player has access to her own smooth local cost function and can communicate to her neighbors in some undirected graph. To deal with fast distributed learning of Nash equilibria under such settings, we introduce a so called augmented game mapping and provide conditions under which this mapping is strongly monotone. We consider a distributed gradient play algorithm for determining a Nash equilibrium (GRANE). The algorithm involves every player performing a gradient step to minimize her own cost function while sharing and retrieving information locally among her neighbors in the network. Using the reformulation of the Nash equilibrium problem based on the strong monotone augmented game mapping, we prove the convergence of this algorithm to a Nash equilibrium with a geometric rate. Furthermore, we introduce the Nesterov type acceleration for the gradient play algorithm. We demonstrate that, similarly to the accelerated algorithms in centralized optimization and variational inequality problems, our accelerated algorithm outperforms GRANE in the convergence rate. Moreover, to relax assumptions required to guarantee the strongly monotone augmented mapping, we analyze the restricted strongly monotone property of this mapping and prove geometric convergence of the distributed gradient play under milder assumptions.

关键词： Games Nash equilibrium Convergence Cost function Acceleration distributed algorithms Symmetric matrices distributed algorithms gradient methods multi-agent systems

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distributed power control algorithms for wireless networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2001年第2期50卷 504-514页

作者： Wu, C Bertsekas, DP Malachite Technol Inc Methuen MA 01844 USA MIT Informat & Decis Syst Lab Cambridge MA 02135 USA

Power control has been shown to be an effective way to increase capacity in wireless systems. In previous work on power control, it has been assumed that power levels can be assigned from a continuous range. In practice, however, power levels are assigned from a discrete set. In this work, we consider the minimization of the total power transmitted over given discrete sets of available power levels subject to maintaining an acceptable signal quality for each mobile, We have developed distributed iterative algorithms for solving a more general version of this integer programming problem, which is of independent interest, and have shown that they find the optimal solution in a finite number of iterations which is polynomial in the number of power levels and the number of mobiles.

关键词： cellular networks distributed algorithms integer programming power control

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distributed disk-based algorithms for model checking very large Markov chains

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FORMAL METHODS IN SYSTEM DESIGN 2006年第2期29卷 177-196页

作者： Bell, Alexander Haverkort, Boudewijn R. Univ Twente Dept Elect Engn Math & Comp Sci NL-7500 AE Enschede Netherlands

In this paper we present data structures and distributed algorithms for CSL model checking-based performance and dependability evaluation. We show that all the necessary computations are composed of series or sums of matrix-vector products. We discuss sparse storage structures for the required matrices and present efficient sequential and distributed disk-based algorithms for performing these matrix-vector products. We illustrate the effectivity of our approach in a number of case studies in which continuous-time Markov chains (generated in a distributed way from stochastic Petri net specifications) with several hundreds of millions of states are solved on a workstation cluster with 26 dual-processor nodes. We show details about the memory consumption, the solution times, and the speedup. The distributed message-passing algorithms have been implemented in a tool called PARSECS, that also takes care of the distributed Markov chain generation and that can also be used for distributed CTL model checking of Petri nets.

关键词： Markov chains matrix-vector product disk-based algorithms state-space generation CSL model checking distributed algorithms

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