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American Control Conference (ACC)

作者： Long, Yanchen Han, Liang Dong, Fei Hu, Qinglei Li, Qingdong Beihang Univ Sino French Engn Sch Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Tianmushan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350382662;9798350382655

This article considers a multiplayer target defense differential game (TDDG) between attacker team and defender team. The attackers aim at reaching the targets while the defenders aim at intercepting their opponents. In the 1 defender vs 1 attacker scenario, a geometric approach is proposed to explicitly obtain the optimal feedback controls of the players and the Value function of the differential game. For a multiplayer game, we propose a bipartite graph matching algorithm, utilizing the Value function, to obtain the optimal assignment. The proposed algorithm performs well in reducing the computational costs. Furthermore, we conduct several simulations to validate the optimality of the proposed method.

关键词： consensus algorithm

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An Optimal algorithm for Sorting Pattern-Avoiding Sequences 65

An Optimal Algorithm for Sorting Pattern-Avoiding Sequences

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65th Symposium on Foundations of Computer Science

作者： Opler, Michal Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9798331516758;9798331516741

We present a deterministic comparison-based algorithm that sorts sequences avoiding a fixed permutation pi in linear time, even if pi is a priori unkown. Moreover, the dependence of the multiplicative constant on the pattern pi matches the information-theoretic lower bound. A crucial ingredient is an algorithm for performing efficient multi-way merge based on the Marcus-Tardos theorem. As a direct corollary, we obtain a linear-time algorithm for sorting permutations of bounded twin-width.

关键词： consensus algorithm

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Study on strategy of wind farm combined with distributed energy storage to realize synergetic-consensus frequency regulation

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JOURNAL OF ENERGY STORAGE 2024年 91卷

作者： Peng, Xiaotao Tan, Junmi Wang, Zehui Mo, Junye Yang, Wanqing Hu, De Key Lab Hubei Prov Integrated Energy Power Syst E Wuhan 430072 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

To optimize the frequency regulation characteristics of wind-storage combined system, this paper proposes a frequency regulation strategy for coordinating wind farm inertia support with distributed energy storage (DES) considering differences in state of charge (SOC). Firstly, a synergetic control strategy for wind turbines (WTs) with different wind speeds is studied to achieve differentiated synergistic inertia support by using variable proportional rotor speed regulation. Then this paper proposes a frequency regulation strategy for DES coordinating with wind farm inertia support based on two-layer synergetic control, which can quickly restore maximum power point tracking (MPPT) operation mode of WTs and avoid the second fall of system frequency. In addition, a consensus power allocation strategy for DES considering the differences in both SOC and power regulation capability is designed in order to allocate frequency regulation power in proportion to SOC and prevent the battery overcharge and over-discharge. Finally, the effectiveness of the proposed strategy for achieving synergetic-consensus frequency regulation is verified in MATLAB/Simulink.

关键词： Wind-storage combined system DES Frequency regulation Synergetic control consensus algorithm

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Intelligent Recommendation algorithm for New Media Content Based on AI Technology 14th

Intelligent Recommendation Algorithm for New Media Content B...

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14th International Conference on Frontier Computing, FC 2024

作者： Lu, Meixing An, Lei Shandong Qingdao266580 China School of Culture and Media Xi’an Eurasia University Shaanxi Xi’an710065 China

ISBN: (纸本)9789819627936

In recent years, with the rapid development of modern technology, people are constantly exposed to recommended new media content through media such as mobile phones. Traditional new media content recommendation algorithms often face insufficient personalization, and the recommended content is also recommended to unwanted audiences, resulting in a lack of targeted recommendations. How to improve the recommendation quality of new media content has become an urgent problem that needs to be solved. Artificial intelligence (AI) algorithm technology can achieve precise recommendations, more accurately capture user new areas, and achieve personalized recommendations, so as to ensure that recommended content is more in line with user preferences, and improve recommendation effectiveness. This article took the FNN (Feed-Forward Network) algorithm model as the research object, integrated it with the KNN (K-Nearest Neighbors) algorithm, and proposed a new ConvFNN-KNN model. It was found that the fused algorithm outperformed other algorithms in terms of accuracy, diversity, and coverage. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： consensus algorithm

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A Simple Parallel algorithm with Near-Linear Work for Negative-Weight Single-Source Shortest Path 8

A Simple Parallel Algorithm with Near-Linear Work for Negati...

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8th SIAM Symposium on Simplicity of algorithms, SOSA 2025

作者： Fischer, Nick Haeupler, Bernhard Latypov, Rustam Roeyskoe, Antti Sulser, Aurelio L. INSAIT Sofia University "St. Kliment Ohridski" Bulgaria ETH Zurich Switzerland Aalto University Finland ETH Zurich Switzerland

ISBN: (纸本)9781611978315

We give the first parallel algorithm with optimal Õ(m) work for the classical problem of computing Single-Source Shortest Paths in general graphs with negative-weight edges. In graphs without negative edges, Dijkstra's algorithm solves the Single-Source Shortest Paths (SSSP) problem with optimal Õ(m) work, but is inherently sequential. A recent breakthrough by [Bernstein, Nanongkai, Wulff-Nilsen;FOCS'22] achieves the same for general graphs. Parallel shortest path algorithms are more difficult and have been intensely studied for decades. Only very recently, multiple lines of research culminated in parallel algorithms with optimal work Õ(m) for various restricted settings, such as approximate or exact algorithms for directed or undirected graphs without negative edges. For general graphs, the best known algorithm by [Ashvinkumar, Bernstein, Cao, Grunau, Haeupler, Jiang, Nanongkai, Su;ESA'24] still requires m1+o(1) work. This paper presents a randomized parallel algorithm for SSSP in general graphs with near-linear work Õ(m) and state-of-the-art span n1/2+o(1). We follow a novel bottom-up approach leading to a particularly clean and simple algorithm. Our algorithm can be seen as a near-optimal parallel black-box reduction from SSSP in general graphs to graphs without negative edges. In contrast to prior works, the reduction in this paper is both parallel and essentially without overhead, only affecting work and span by polylogarithmic factors. Copyright © 2025 Copyright for this paper is retained by authors.

关键词： consensus algorithm

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Investigating Trends in Interactive Communication Media algorithms for Networking Applications 5th

Investigating Trends in Interactive Communication Media Algo...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Chaudhary, Anamika Nachappa, M.N. Singh, Swati Jain, Sandeep Kumar Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

This research paper investigates the developments in interactive verbal exchange media algorithms for networking programs. It begins with a discussion on cutting-edge algorithms, followed by means of an exam of the various tactics proposed for reliable media streaming techniques. Next, the focal point shifts to discussing the benefits of media algorithms for networking packages. This includes a discussion of how algorithms permit easier control and control of networks and the way they’re used for real-time communiqué. Subsequently, the paper concludes by means of highlighting challenges and opportunities for the software of media algorithms and makes guidelines for in addition studies. This research paper affords a complete evaluate at the current nation of media algorithms and their packages in networking applications. This technical abstract investigates the traits in interactive communiqué media algorithms for networking packages. in particular, the look at appears at three distinctive media algorithms, namely social media algorithms, usage algorithms, and media notion algorithms, and investigates the results of those algorithms on the layout and implementation of networking packages. The use of those developments, a conceptual version is evolved, including a web content material company, an interactive person, and a third-party facts aggregator. The ensuing version is then applied to all 3 media algorithms to decide the effect on networking programs’ overall performance. Effects indicate that the exclusive media algorithms can lead to numerous differences in network overall performance while considering latency, throughput, and fine of carrier. The consequences for networking applications advocate that the right choice and use of interactive conversation media algorithms can cause multiplied person satisfaction and top of the line community overall performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： consensus algorithm

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Revisiting Bagging for Stochastic algorithms 37th

Revisiting Bagging for Stochastic Algorithms

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Owen, Caitlin A. Dick, Grant Whigham, Peter A. School of Computing University of Otago Dunedin New Zealand

ISBN: (纸本)9789819603503

Bagging is a commonly used ensemble method, which involves the bootstrap sampling of training observations. Bootstraps are typically needed to decouple the models. However, this is based on the well-understood properties of deterministic algorithms. To perform bagging for stochastic algorithms, there is an implicit assumption that both variance due to the training data and variance due to the algorithm need to be reduced. This assumption may not be correct and only a reduction in variance due to the algorithm may need to be targeted. Bootstrapping may cause an unnecessary increase in error due to bias because it reduces the number of unique training observations. Bootstrapped and non-bootstrapped ensembles are compared across multiple machine learning algorithms and data sets using an extended error decomposition. The results show that bootstrap sampling is often not required as it increases error due to bias and internal variance. The prediction error associated with an algorithm and data set needs to be more fully decomposed in order to distinguish between the different sources of error. This allows the most appropriate ensemble method to be chosen. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： consensus algorithm

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Fast Deterministic Massively Parallel Ruling Sets algorithms 25

Fast Deterministic Massively Parallel Ruling Sets Algorithms

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26th International Conference on Distributed Computing and Networking, ICDCN 2025

作者： Ji, Hongyan Kothapalli, Kishore Pemmaraju, Sriram V Singh, Ajitanshu Department of Computer Science The University of Iowa Iowa CityIA United States Center for Security Theory and Algorithmic Research IIIT Hyderabad Telangana Hyderabad India

ISBN: (纸本)9798400710629

In this paper, we present a deterministic Õ(log1/3)-round algorithm for the 2-ruling set problem in the sublinear Massively Parallel Computation (MPC) model. This improves upon the fastest known deterministic 2-ruling set algorithm for this model, which is the Õ(√log;n)-round algorithm by Giliberti and Parsaeian (PODC 2024). Our result is obtained by derandomizing the "sample-and-gather"approach of Kothapalli, Pai, and Pemmaraju (FSTTCS 2020). The "sample-and-gather"approach involves making random sampling decisions, not just for the current iteration, but a batch of future iterations. Thus, derandomizing this approach requires the "fixing"of randomness for a batch of future iterations. We further extend our results to show that a β-ruling set for β ≥ 2 can be obtained in Õ(log1/2β - 1 Δ) deterministic rounds in the sublinear MPC model. Additionally, we present a deterministic β-ruling set algorithms for sparse graphs (i.e., bounded arboricity graphs) where β ≥ 2, which runs in Õ(log1/2β - 1 λ) rounds for arboricity-λ graphs in the sublinear MPC model. © 2025 Copyright held by the owner/author(s).

关键词： consensus algorithm

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Tight Approximation Bounds on a Simple algorithm for Minimum Average Search Time in Trees 22nd

Tight Approximation Bounds on a Simple Algorithm for Mini...

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22nd International Workshop on Approximation and Online algorithms, WAOA 2024

作者： Høgemo, Svein University of Bergen Bergen Norway

ISBN: (纸本)9783031813955

The graph invariant EPT-sum has cropped up in several unrelated fields in later years: As an objective function for hierarchical clustering, as a more fine-grained version of the classical edge ranking problem, and, specifically when the input is a vertex-weighted tree, as a measure of average/expected search length in a partially ordered set. The EPT-sum of a graph G is defined as the minimum sum of the depth of every leaf in an edge partition tree (EPT), a rooted tree where leaves correspond to vertices in G and internal nodes correspond to edges in G. A simple algorithm that approximates EPT-sum on trees is given by recursively choosing the most balanced edge in the input tree G to build an EPT of G. Due to its fast runtime, this balanced cut algorithm can be used in practice, and has earlier been analysed to give a 1.62-approximation on trees. In this paper, we show that the balanced cut algorithm gives a 1.5-approximation of EPT-sum on trees, which amounts to a tight analysis and answers a question posed by Cicalese et al. in 2014. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： consensus algorithm

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A Simple algorithm for Worst Case Optimal Join and Sampling 28

A Simple Algorithm for Worst Case Optimal Join and Sampling

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28th International Conference on Database Theory, ICDT 2025

作者： Capelli, Florent Irwin, Oliver Salvati, Sylvain Université d’Artois CNRS UMR 8188 - CRIL LensF-62300 France Université de Lille CNRS Inria UMR 9189 - CRIStAL LilleF-59000 France

ISBN: (纸本)9783959773645

We present an elementary branch and bound algorithm with a simple analysis of why it achieves worstcase optimality for join queries on classes of databases defined respectively by cardinality or acyclic degree constraints. We then show that if one is given a reasonable way for recursively estimating upper bounds on the number of answers of the join queries, our algorithm can be turned into algorithm for uniformly sampling answers with expected running time Õ(UP/OUT) where UP is the upper bound, OUT is the actual number of answers and Õ(·) ignores polylogarithmic factors. Our approach recovers recent results on worstcase optimal join algorithm and sampling in a modular, clean and elementary way. © Florent Capelli, Oliver Irwin, and Sylvain Salvati.

关键词： consensus algorithm

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