We consider the stochastic generalized Nash equilibrium problem (SGNEP) with joint feasibility constraints and expected–value cost functions. We propose a distributed stochastic projected reflected gradient algorithm...
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We consider the stochastic generalized Nash equilibrium problem (SGNEP) with joint feasibility constraints and expected–value cost functions. We propose a distributed stochastic projected reflected gradient algorithm and show its almost sure convergence when the pseudogradient mapping is monotone and the solution is unique. The algorithm is based on monotone operator splitting methods tailored for SGNEPs when the expected-value pseudogradient mapping is approximated at each iteration via an increasing number of samples of the random variable. Finally, we show that a preconditioned variant of our proposed algorithm has convergence guarantees when the pseudogradient mapping is cocoercive.
Recently, a quantum algorithm called Quantum Fuzzy Inference Engine (QFIE) has been introduced with the main goal of providing exponential speedup in the execution of a Mamdani fuzzy inference engine. This quantum alg...
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ISBN:
(纸本)9798350332285
Recently, a quantum algorithm called Quantum Fuzzy Inference Engine (QFIE) has been introduced with the main goal of providing exponential speedup in the execution of a Mamdani fuzzy inference engine. This quantum algorithm achieves this result by modeling a fuzzy rule base with a quantum oracle, a black box that is widely used to estimate functions using quantum mechanical principles. Although QFIE offers the possibility of performing efficient fuzzy computation on quantum computers, its real-world applicability is limited by the high levels of noise still present in current quantum computers. Consequently, it is necessary to introduce technological arrangements to make QFIE fully functional in practical cases until noisefree quantum computers are released. This paper addresses this issue by designing a distributed version of QFIE, based on the D-NISQ reference model, to distribute the computation of subsets of fuzzy rules across multiple quantum processors and minimize the negative impact of quantum noise. Experimental results prove that this distributed version of QFIE is able to significantly improve the accuracy of fuzzy computation on quantum devices, making QFIE applicable in real-world scenarios.
The locality of a graph problem is the smallest distance T such that each node can choose its own part of the solution based on its radius -T neighborhood. In many settings, a graph problem can be solved efficiently w...
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The locality of a graph problem is the smallest distance T such that each node can choose its own part of the solution based on its radius -T neighborhood. In many settings, a graph problem can be solved efficiently with a distributed or parallel algorithm if and only if it has a small locality. In this work we seek to automate the study of solvability and locality: given the description of a graph problem II, we would like to determine if II is solvable and what is the asymptotic locality of II as a function of the size of the graph. Put otherwise, we seek to automatically synthesize efficient distributed and parallel algorithms for solving II. We focus on locally checkable graph problems;these are problems in which a solution is globally feasible if it looks feasible in all constant-radius neighborhoods. Prior work on such problems has brought primarily bad news: questions related to locality are undecidable in general, and even if we focus on the case of labeled paths and cycles, determining locality is PSPACE-hard (Balliu et al., PODC 2019). We complement prior negative results with efficient algorithms for the cases of unlabeled paths and cycles and, as an extension, for rooted trees. We study locally checkable graph problems from an automata-theoretic perspective by representing a locally checkable problem II as a nondeterministic finite automaton M over a unary alphabet. We identify polynomial-time-computable properties of the automaton M that near-completely capture the solvability and locality of II in cycles and paths, with the exception of one specific case that is co-NP-complete.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).
An open problem is to extend the results in the literature on unit disk graphs to hypergraph models. Motivated by recent results that the worst-case performance of the distributed maximal scheduling algorithm is chara...
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ISBN:
(纸本)9781450397964
An open problem is to extend the results in the literature on unit disk graphs to hypergraph models. Motivated by recent results that the worst-case performance of the distributed maximal scheduling algorithm is characterized by the interference degree of the hypergraph, in the present work we investigate properties of the interference degree of the hypergraph and the structure of hypergraphs arising from physical constraints. We show that the problem of computing the interference degree of a hypergraph is NP-hard and we prove some properties and results concerning this hypergraph invariant. We then investigate which hypergraphs are realizable, i.e. which hypergraphs arise in practice, based on physical constraints, as the interference model of a wireless network. In particular, given the results on the worst-case performance of the maximal scheduling algorithm, a question that arises naturally is: what is the maximal value of r such that the hypergraph K-1,K-r is realizable? We show that this value is r = 4.
Reliability analysis is of great significance to designing and maintaining wireless multi-hop networks (WMhNs). In WMhNs, several reasons can cause a node to be inoperable, such as hardware failure, software errors, a...
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Reliability analysis is of great significance to designing and maintaining wireless multi-hop networks (WMhNs). In WMhNs, several reasons can cause a node to be inoperable, such as hardware failure, software errors, and battery drain. Failure of some critical nodes may partition networks into disconnected segments. The presence of such critical nodes may also reduce the network lifetime since they consume more energy for packet forwarding. Therefore, it is crucial to identify the critical nodes of the networks and strengthen them by adding more nodes surrounding those or creating alternate pathways connecting other nodes to ensure connectivity maintenance in WMhNs. One of the most common approaches in direction is detecting the cut nodes of the networks. However, although finding cut nodes provide helpful information, it may be insufficient for precise reliability analysis since finding cut nodes only does not consider the remaining network. Critical Node Problem (CNP) aims to detect the most important nodes of the network whose removal minimizes the pairwise connectivity (the total number of node pairs connected by at least one path). In other words, the CNP tries to identify a set of nodes whose absence partitions the network into several disconnected segments of similar size. Detecting critical nodes for pairwise connectivity reveals the weak points and bottlenecks of the networks and may help to increase the fault tolerance and lifetime of WMhNs. This paper proposes an Asynchronous distributed Algorithm for minimizing Pairwise Connectivity (ADA-PC) in WMhNs. To the best of our knowledge, this is the first distributed algorithm for the targeted problem in the network literature. The proposed algorithm uses a distributed Breadth-First Search (BFS) tree which limits bit complexity to O(***2n) and space complexity to O(d), where d is the network's diameter. The experimental study on both testbed experiment and simulation reveals that the proposed algorithm is ca
This letter deals with linear algebraic equations where the global coefficient matrix and constant vector are given respectively, by the summation of the coefficient matrices and constant vectors of the individual age...
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This letter deals with linear algebraic equations where the global coefficient matrix and constant vector are given respectively, by the summation of the coefficient matrices and constant vectors of the individual agents. Our approach is based on reformulating the original problem as an unconstrained optimization. Based on this exact reformulation, we first provide a gradient-based, centralized algorithm which serves as a reference for the ensuing design of distributed algorithms. We propose two sets of exponentially stable continuous-time distributed algorithms that do not require the individual agent matrices to be invertible, and are based on estimating non-distributed terms in the centralized algorithm using dynamic average consensus. The first algorithm works for time-varying weight-balanced directed networks, and the second algorithm works for general directed networks for which the communication graphs might not be balanced. Numerical simulations illustrate our results.
In this paper, we initiate the study of the Mutual Visibility problem using oblivious luminous point robots that have inaccurate movements. Robots are opaque i.e., two robots see each other only if the line segment co...
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ISBN:
(数字)9783031488825
ISBN:
(纸本)9783031488818;9783031488825
In this paper, we initiate the study of the Mutual Visibility problem using oblivious luminous point robots that have inaccurate movements. Robots are opaque i.e., two robots see each other only if the line segment connecting them contains no robots. Each robot operates in Look-Compute-Move cycles. A robot has a light attached to it. We define the inaccuracy in the movement of a robot r as a deviation from its target point T to a point T ' such that angle TrT ' < 90 degrees. From any initial configuration of the robots on the Euclidean plane, the aim of the problem is to arrange the robots in a configuration such that any two robots are visible to each other. We assume that the robots agree on one coordinate axis. We present a collision-free algorithm that uses 3 colors and runs in O(N) epoch under asynchronous setting, where N is the number of robots. An epoch is the smallest time interval in which all robots get activated and execute LCM cycle at least once. We also study the problem in presence of faulty robots, where by fault, we mean mobility failure in which the robots become immobile after fault. This fault does not affect the light of the robots. Any robot can encounter fault at any time. Moreover, a robot can be faulty along with exhibiting inaccuracy in its movement. We also present a fault-tolerant algorithm which aims to bring the robots in a configuration where no three non-faulty robots can collinear and no faulty robot lies between two non-faulty robot. We prove that the non-faulty robots achieve mutual visibility in O(N) epochs under asynchronous settings.
The sequential pattern mining was widely used to solve various business problems, including frequent user click pattern, customer analysis of buying product, gene microarray data analysis, etc. Many studies were going...
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The sequential pattern mining was widely used to solve various business problems, including frequent user click pattern, customer analysis of buying product, gene microarray data analysis, etc. Many studies were going on these pattern mining to extract insightful data. All the studies were mostly concentrated on high utility sequential pattern mining (HUSP) with positive values without a distributed approach. All the existing solutions are centralized which incurs greater computation and communication costs. In this paper, we introduce a novel algorithm for mining HUSPs including negative item values in support of a distributed approach. We use the Hadoop map reduce algorithms for processing the data in parallel. Various pruning techniques have been proposed to minimize the search space in a distributed environment, thus reducing the expense of processing. To our understanding, no algorithm was proposed to mine High Utility Sequential Patterns with negative item values in a distributed environment. So, we design a novel algorithm called DHUSP-N (distributed High Utility Sequential Pattern mining with Negative values). DHUSP-N can mine high utility sequential patterns considering the negative item utilities from Bigdata.
This letter considers the problem of multi-agent distributed linear regression in the presence of system noises. In this problem, the system comprises multiple agents wherein each agent locally observes a set of data ...
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This letter considers the problem of multi-agent distributed linear regression in the presence of system noises. In this problem, the system comprises multiple agents wherein each agent locally observes a set of data points, and the agents' goal is to compute a linear model that best fits the collective data points observed by all the agents. We consider a server-based distributed architecture where the agents interact with a common server to solve the problem;however, the server cannot access the agents' data points. We consider a practical scenario wherein the system either has observation noise, i.e., the data points observed by the agents are corrupted, or has process noise, i.e., the computations performed by the server and the agents are corrupted. In noise-free systems, the recently proposed distributed linear regression algorithm, named the Iteratively Pre-conditioned Gradient-descent (IPG) method, has been claimed to converge faster than related methods. In this letter, we study the robustness of the IPG method, against both the observation noise and the process noise. We empirically show that the robustness of the IPG method compares favorably to the state-of-the-art algorithms.
The Internet of Things (IoT) is rapidly gaining ground in future wireless communications. Transmission reliability and latency are two significant measurements for the utilization of the IoT. In this paper, we aim to ...
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The Internet of Things (IoT) is rapidly gaining ground in future wireless communications. Transmission reliability and latency are two significant measurements for the utilization of the IoT. In this paper, we aim to improve reliability and latency requirements by solving the link scheduling problem. Under the Rayleigh fading model, a more realistic interference model, we first localize the global interference by ignoring the interference outside some certain distance, and obtain the success probability of a transmission at least 1 - epsilon, where epsilon is an acceptable error probability of a transmission. Based on this key result, we then design two localized and distributed algorithms for one-slot scheduling problem (i.e., how to ensure that the selected links have high transmission reliability or can be scheduled successfully). In addition, we design a localized and distributed algorithm with time complexity of O(Delta('T,r)(max) log n) to resolve latency minimization problem (i.e., minimize the number of time slots until all transmissions were successful), where Delta('T,r)(max) is the maximum number of senders within R-T centered at a receiver in the network, where R-T is transmission range of a node. Theoretical analysis and extensive simulations demonstrate that the proposed algorithms can improve the reliability and latency requirements significantly. (C) 2019 Elsevier B.V. All rights reserved.
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