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Toward Rapid and Optimal Strategy for Swarm Conflict: A Computational Game Approach

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第3期60卷 3108-3120页

作者： Zhang, Tao Zhu, Yiji Ma, Dongying Li, Chaoyong Wang, Xiaodong Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Beijing Inst Elect Syst Engn Beijing 100854 Peoples R China

The decision and control problem for swarm operations is crucial for autonomous military conflict management. In this article, the underlying decision and control problem is treated as a noncooperative game problem, in which the underlying target assignment problem is generalized to be a graph-theoretic problem. We introduce an algorithm to seek the desired Nash equilibrium with the help of the parallel maximum weight matching algorithm. Then, we prove that the proposed solution is epsilon-Nash with guaranteed computational efficiency, and is well suited for the swarm conflict. Simulation results verified the effectiveness of the proposed solutions.

关键词： Games approximation algorithms Computational modeling Military computing Analytical models Nash equilibrium Task analysis Game theory search algorithm swarm operation

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Distributed Policy Space Response Oracles in Two-Player Zero-Sum Games

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第6期36卷 9893-9904页

作者： Tang, Hongsong Liu, Yingzhuo Ni, Letian Xiang, Liuyu Yang, Yaodong Bi, Ke He, Zhaofeng Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Peking Univ Inst Artificial Intelligence Beijing 100876 Peoples R China

Policy space response oracle (PSRO) is a population-based algorithm that can be used to solve two-player zero-sum games. In the PSRO solution framework, optimizing policy diversity is crucial for addressing nontransitive game problems, helping the agent population avoid exploitation by unfamiliar opponents. In addition, while deep reinforcement learning is highly effective in solving complex game environments, its integration with PSRO remains fragmented and lacking in effective coordination. In this study, we propose distributed PSRO to efficiently solve complex game scenarios. To enhance diversity while managing optimization costs, we introduce TOP-K truncation, which prioritizes high-quality opponents and limits the size of the policy pool during sampling. This approach not only reduces interference from less effective strategies but also ensures computational efficiency by seamlessly integrating with our distributed training framework. We also design the distributed training framework to incorporate diversity estimation directly into the sampling process, achieving diversity optimization without incurring additional computational overhead. Furthermore, we introduce the opponent first (OF) method, which enhances decision-making by leveraging opponent information during interaction sampling. We perform experimental validation using a nontransitive mixture model and AlphaStar888 to confirm the effectiveness of the TOP-K truncation approach. Finally, we demonstrate the feasibility and efficiency of the distributed training framework and the OF approach in a Google Research Football 11 versus 11 scenario.

关键词： Games Training Optimization Game theory Deep reinforcement learning Computational efficiency Costs approximation algorithms Telecommunications Sports distributed system neural network self-play zero-sum game

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Model-Driven IEP-GNN Framework for MIMO Detection With Bayesian Optimization

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第2期13卷 387-391页

作者： Liu, Zishen He, Dongxuan Wu, Nan Yan, Qinsiwei Li, Yonghui Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2008 Australia

In this letter, a model-driven detector called IEP-GNN is proposed for massive multiple-input and multiple-output (MIMO) systems. Graph neural network (GNN) and improved moment matching (IMM) are integrated into the expectation propagation (EP) algorithm to improve the accuracy of posterior distribution approximation and leverage the self-correction ability of EP algorithm. Moreover, to acquire the training experiences and optimize initial parameters, hotbooting and Bayesian parameter optimization (BPO) are employed respectively, which can further improve the performance of the proposed IEP-GNN. Simulation results show that our proposed IEP-GNN with BPO outperforms other state-of-the-art EP-based detectors while maintaining an acceptable convergence and computational complexity.

关键词： Detectors Graph neural networks Optimization Computational modeling Bayes methods approximation algorithms Transmitting antennas Model-driven expectation propagation graph neural network Bayesian parameter optimization

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approximation algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover 14

Approximation Algorithms for Stochastic Boolean Function Eva...

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Amol Deshpande Lisa Hellerstein Devorah Kletenik University of Maryland Polytechnic Institute of NYU

ISBN: (纸本)9781611973389

We present approximation algorithms for two problems: Stochastic Boolean Function Evaluation (SBFE) and Stochastic Submodular Set Cover (SSSC). Our results for SBFE problems are obtained by reducing them to SSSC problems through the construction of appropriate utility functions. We give a new algorithm for the SSSC problem that we call Adaptive Dual Greedy. We use this algorithm to obtain a 3-approximation algorithm solving the SBFE problem for linear threshold formulas. We also get a 3- approximation algorithm for the closely related Stochastic Min-Knapsack problem, and a 2-approximation for a natural special case of that problem. In addition, we prove a new approximation bound for a previous algorithm for the SSSC problem, Adaptive Greedy. We consider an approach to approximating SBFE problems using existing techniques, which we call the Q-value approach. This approach easily yields a new result for evaluation of CDNF formulas, and we apply variants of it to simultaneous evaluation problems and a ranking problem. However, we show that the Q-value approach provably cannot be used to obtain a sublinear approximation factor for the SBFE problem for linear threshold formulas or read-once DNF.

关键词： approximation algorithms Static synchronous series compensator Stochastic Evaluation Problems Q-VALUE Boolean functions disjunctive normal form algorithms formula

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An approximation Algorithm for Path Planning of Vehicles for Data Collection in Wireless Rechargeable Sensor Networks 23

An Approximation Algorithm for Path Planning of Vehicles for...

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24th International Conference on Distributed Computing and Networking (ICDCN)

作者： Kumar, Rohit Mukherjee, Joy Chandra Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar Odisha India

ISBN: (纸本)9781450397964

Wireless rechargeable sensor networks (WRSNs) have been extensively used in various event detection and environmental monitoring applications, where it is important to collect environmental data from the sensors. Data may be collected at the base station (BS) through multi-hop communication. But it may cause sensors located near the BS to run out of their energy quickly. In a WRSN, data may also be collected using mobile vehicles that visit different sensors to collect data. However, in a large scale WRSN, travelling to all the sensors for data collection may cause vehicles to run out of their energy in the middle of their journey. In this paper, we formulate an optimization problem that minimizes the average travel distance of the vehicles while collecting data from maximum number of sensors. The problem is proved to be NP-complete. To avoid visiting all the sensors, our scheme selects a subset of sensors as anchor nodes that first collects data from neighboring sensors through one-hop transfer. Then, the anchor nodes are divided among the vehicles. Finally, a convex-hull based 3-approximation algorithm is proposed that finds the travel plan for each vehicle through the anchor nodes only, where the vehicle starts from the BS, collects data from the anchor nodes, while returning back to the BS at the end of its journey. Simulation results show that our scheme outperforms the existing schemes in terms of the average travel distance by vehicles, while collecting data from a large number of sensors.

关键词： approximation algorithms Data Collection Wireless rechargeable sensor networks

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Sectoring Approach for Performance Enhancement of MTT and Its Application on JPDA Algorithm

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IEEE ACCESS 2025年 13卷 67758-67768页

作者： Cobankaya, Mustafa Latif Elrouby, Alaa B. Secretariat Def Ind TR-06420 Ankara Turkiye Ankara Yildirim Beyazit Univ Elect & Elect Engn Dept TR-06010 Ankara Turkiye

Recent advancements in multi-target tracking (MTT) technologies and algorithms have led to improved accuracy and efficiency in tracking multiple objects in dynamic environments. However, as the number of targets increases, MTT methods often struggle with computational demands and tracking capacity. This paper presents a novel sectoring approach on the Joint Probabilistic Data Association (JPDA) algorithm to enhance its tracking performance in MTT scenarios. The sectoring method divides the tracking area into distinct sectors and assigns a separate JPDA tracker for each sector, yielding two primary benefits in environments with large numbers of targets. First, it increases the capacity of the JPDA tracker to handle a larger number of targets. Second, it reduces computational complexity when tracking a high number of targets. On the other hand, the approach introduces a trade-off by slightly increasing computational complexity in scenarios with fewer targets. Experimental results were obtained through simulations of 10 different scenarios, each with 37 varying numbers of targets and three different sector configurations, amounting to a total of 1110 simulations. The findings demonstrate that the sectoring approach achieves up to a 95% reduction in computational complexity and increases the number of tracked targets from 24 to 40 in randomly generated scenarios. Results also revealed that the effects of the sectoring approach become more pronounced as the number of sectors increases.

关键词： Target tracking Signal processing algorithms Radar tracking Kalman filters Computational complexity Heuristic algorithms Computational modeling approximation algorithms Surveillance Data models Multi-target tracking joint probabilistic data association computational complexity high number of maneuvering targets sectoring approach

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Fast and Accurate Log-Determinant approximations

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1520-1524页

作者： Deen, Owen Waller, Colton River Ward, John Paul Univ North Carolina Wilmington Dept Math & Stat Wilmington NC 28403 USA North Carolina A&T State Univ Dept Math & Stat Greensboro NC 27408 USA

We consider the problem of estimating log-determinants of large, sparse, positive definite matrices. A key focus of our algorithm is to reduce computational cost, and it is based on sparse approximate inverses. The algorithm can be implemented to be adaptive, and it uses graph spline approximation to improve accuracy. We illustrate our approach on classes of large sparse matrices.

关键词： Sparse matrices Splines (mathematics) approximation algorithms Indexes Signal processing algorithms Laplace equations Vectors Graph models graph splines log-determinant positive definite sparse matrices

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2-approximation algorithm for minmax absolute maximum lateness scheduling-location problem

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OPERATIONS RESEARCH LETTERS 2022年第6期50卷 732-737页

作者： Krumke, Sven O. Le, Huy Minh Tech Univ Kaiserslautern Dept Math Paul Ehrlich Str 14 D-67663 Kaiserslautern Germany Van Lang Univ Fac Fundamental Sci 69 68 Dang Thuy Tram St Ward 13 Ho Chi Minh City Vietnam

The problem of minmax absolute scheduling-location is investigated on trees with interval edge length. Jobs are located at vertices and must travel to the machine. The goal is to find a machine location and simultaneously a schedule to minimize the maximum lateness in the worst-case. We derive a result that could reduce the robust versions to deterministic problems. An efficient algorithm is developed to solve special cases. A 2-approximation algorithm is proposed for the robust problem on the underlying tree.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Scheduling-location approximation algorithms Uncertainty Robust optimization Lateness

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PauliForest: Connectivity-Aware Synthesis and Pauli-Oriented Qubit Mapping for Near-Term Quantum Simulation

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第6期44卷 2119-2129页

作者： Li, Yongshang Zhang, Yu Deng, Haoning Chen, Mingyu Li, Zhenyu Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Univ Sci & Technol China Hefei Natl Lab Hefei 230088 Peoples R China Univ Sci & Technol China Sch Chem & Mat Sci Dept Chem Phys Hefei 230027 Peoples R China

Quantum simulation is the foundation for the design of many algorithms which share subroutines known as quantum simulation kernels. Optimizing the compilation of these kernels is crucial, involving two key components: 1) circuit synthesis and 2) qubit mapping. However, existing circuit synthesis methods either overlook qubit connectivity constraints (QCCs) or prioritize minimizing gate count over optimizing circuit depth. Similarly, current qubit mapping techniques do not work well with circuit synthesis methods. To address these limitations, we propose PauliForest, which comprises a connectivity-aware circuit synthesis algorithm and a Pauli-oriented qubit mapping algorithm. The synthesis algorithm employs heuristic strategies to generate shallower circuits, while the qubit mapping algorithm seamlessly collaborates with the circuit synthesis process. Compared to the state-of-the-art Paulihedral compiler, our approach significantly reduces both CNOT gate counts (by 13%) and circuit depths (by 25%). Experiments on a noisy simulator and a real superconducting quantum computer show that our algorithm can improve the fidelity of quantum circuit execution compared to Paulihedral.

关键词： Qubit Logic gates Circuit synthesis Quantum simulation Kernel Computers approximation algorithms Quantum algorithm Design automation Transforms quantum simulation qubit mapping

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Anti-differentiating approximation algorithms: A case study with min-cuts, spectral, and flow 31

Anti-differentiating approximation algorithms: A case study ...

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31st International Conference on Machine Learning, ICML 2014

作者： Gleich, David F. Mahoney, Michael W. Computer Science Purdue University West LafayetteIN47906 United States Dept. of Statistics International Computer Science Institute University of California at Berkeley BerkeleyCA94720 United States

ISBN: (纸本)9781634393973

We formalize and illustrate the general concept of algorithmic anti-differentiation: given an algorithmic procedure, e.g., an approximation algorithm for which worst-case approximation guarantees are available or a heuristic that has been engineered to be practically-useful but for which a precise theoretical understanding is lacking, an algorithmic anti-derivative is a precise statement of an optimization problem that is exactly solved by that procedure. We explore this concept with a case study of approximation algorithms for finding locally-biased partitions in data graphs, demonstrating connections between min-cut objectives, a personalized version of the popular PageRank vector, and the highly effective "push" procedure for computing an approximation to personalized PageRank. We show, for example, that this latter algorithm solves (exactly, but implicitly) an l1 -regularized l2-regression problem, a fact that helps to explain its excellent performance in practice. We expect that, when available, these im-plicit optimization problems will be critical for rationalizing and predicting the performance of many approximation algorithms on realistic data. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： approximation algorithms

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