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检索条件"主题词=Consensus algorithm"

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Multiplicative Weights Update, Area Convexity and Random Coordinate Descent for Densest Subgraph Problems 41

Multiplicative Weights Update, Area Convexity and Random Coo...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Nguyen, Ta Duy Ene, Alina Department of Computer Science Boston University United States

We study the densest subgraph problem and give algorithms via multiplicative weights update and area convexity that converge in O(log m/ϵ2) and O (log m/ϵ) iterations, respectively, both with nearly-linear time per iteration. Compared with the work by Bahmani et al. (2014), our MWU algorithm uses a very different and much simpler procedure for recovering the dense subgraph from the fractional solution and does not employ a binary search. Compared with the work by Boob et al. (2019), our algorithm via area convexity improves the iteration complexity by a factor ∆-the maximum degree in the graph, and matches the fastest theoretical runtime currently known via flows (Chekuri et al., 2022) in total time. Next, we study the dense subgraph decomposition problem and give the first practical iterative algorithm with linear convergence rate O (mn log 1/ϵ) via accelerated random coordinate descent. This significantly improves over O (m√mn∆/ϵ) time of the FISTA-based algorithm by Harb et al. (2022). In the high precision regime ϵ 1/n where we can even recover the exact solution, our algorithm has a total runtime of O (mn log n), matching the exact algorithm via parametric flows (Gallo et al., 1989). Empirically, we show that this algorithm is very practical and scales to very large graphs, and its performance is competitive with widely used methods that have significantly weaker theoretical guarantees. Copyright 2024 by the author(s)

关键词： consensus algorithm

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On three fundamental graph enumeration problems and the corresponding graph generation algorithms 17

On three fundamental graph enumeration problems and the corr...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Bossard, Antoine Kanagawa University Graduate School of Science Yokohama221-8686 Japan

ISBN: (纸本)9798350351637

Green supercomputing has been attracting more and more attention due to the acknowledgement of environmental issues. This is especially the case in the field of supercomputing: given the large number of compute nodes involved, the power consumption of these machines is high, in the order of tens of megawatts. Efficient usage of supercomputers is thus key to minimise their power consumption, and one approach is to increase data transfer rates so that the computer can be returned into an energy efficient sleep mode rapidly. Parallel paths and especially disjoint paths routing is one solution to achieve such more efficient data transfers. Yet, the selection of mutually disjoint paths is complex, and in some cases graph enumeration can produce a solution whereas conventional approaches fail. The contribution of this paper is twofold: for the selected three fundamental graph enumeration problems, we give simple, easy to understand proofs that do not involve complex mathematical notions as in previous works;we describe and evaluate the corresponding graph generation algorithms and make comparisons to straightforward or conventional approaches. Not only theoretical evaluation but also empirical evaluation is conducted. Time complexity is shown to be optimal. © 2024 IEEE.

关键词： consensus algorithm

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Research on Online Shopping Recommendation algorithm Based on Apriori algorithm with Association Rules 24

Research on Online Shopping Recommendation Algorithm Based o...

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2024 International Conference on Intelligent Education and Computer Technology, IECT 2024

作者： Zhou, Shuang Sultan Idris Education University Tanjung Malim Malaysia

ISBN: (纸本)9798400709920

With the rapid development of e-commerce, online shopping has become an indispensable part of people's daily lives. However, in the face of vast amounts of product information, how to provide personalized product recommendations to users has become a pressing issue. The Apriori algorithm, as a classic association rule mining algorithm, is widely used in multiple fields. This study explores a recommendation algorithm for online shopping based on the Apriori algorithm, and combines it with collaborative filtering technology to propose a hybrid recommendation algorithm model. Through experimental design and analysis validation, this model demonstrates superior performance in recommending to new users and new items compared to a single recommendation algorithm. The experimental results indicate that the hybrid recommendation algorithm, which combines user behavior data with product attributes, can effectively enhance the performance of the recommendation system, offering users more precise and personalized product recommendations. © 2024 ACM.

关键词： consensus algorithm

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The PACE 2024 Parameterized algorithms and Computational Experiments Challenge: One-Sided Crossing Minimization 19

The PACE 2024 Parameterized Algorithms and Computational Exp...

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19th International Symposium on Parameterized and Exact Computation, IPEC 2024

作者： Kindermann, Philipp Klute, Fabian Terziadis, Soeren Trier University Germany Polytechnic University of Catalonia Barcelona Spain Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773539

This article is a report by the challenge organizers on the 9th Parameterized algorithms and Computational Experiments Challenge (PACE 2024). As was common in previous iterations of the competition, this year’s iteration implemented an exact and heuristic track for a parameterized problem that has gained attention in the theory community. This year’s challenge is about the One-Sided Crossing Minimization Problem (OSCM). In the exact track, the competition participants were asked to develop an exact algorithm that can solve as many instances as possible from a benchmark set of 100 instances - with a time limit of 30 minutes per instance. In the heuristic track, the task must be accomplished within 5 minutes, however, the result in this track is not required to be optimal. New this year is the parameterized track, which has the same rules as the exact track, but instances are guaranteed to have small cutwidth. As in previous iterations, the organizers handed out awards to the best solutions in all tracks and to the best student submissions. © Philipp Kindermann, Fabian Klute, and Soeren Terziadis.

关键词： consensus algorithm

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An argumentation interface to facilitate human-machine collaboration in scientific research: A preliminary exploration 24

An argumentation interface to facilitate human-machine colla...

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24th Workshop on Computational Models of Natural Argument, CMNA 2024

作者： Fu, Yuanxi Schneider, Jodi School of Information Sciences University of Illinois at Urbana-Champaign 501 E. Daniel Street ChampaignIL61820 United States

In today’s increasingly data- and AI-driven scientific research enterprise, the division of labor between computational methods development and usage poses great risks of misuse. Moreover, flaws in a method can be uncovered years after the method’s wide adoption, raising questions about how to share the information about uncovered flaws with new generations of users in a timely manner. We propose an argumentation interface to meet these challenges. Using a toy example of choosing methods for ranking players in a chess tournament, we demonstrate how argumentation frameworks—which will form a key component in our argumentation interface—can be used to organize arguments about the choice of method. We lay out our plan to use the case of two prominent community detection algorithms to determine whether AFs are equally applicable to organizing arguments about choosing methods for scientific research tasks. © 2024 Copyright for this paper by its authors.

关键词： consensus algorithm

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Optimization and Performance Evaluation of Personalized algorithm in Music Recommendation System 3

Optimization and Performance Evaluation of Personalized Algo...

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3rd International Conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Chong St.Paul University Manila Philippines

ISBN: (纸本)9798331519032

Music recommendation system (MRS) is a system that recommends music content for users based on their historical behavior and interests. Personalized algorithm is the core technology of MRS, which provides personalized music recommendation for users by analyzing their behaviors and interests. However, there are some problems in the current personalized algorithm, such as too much concentration of recommendation results and lack of diversity of recommendation results. Therefore, it is of great significance to optimize and evaluate the performance of personalized algorithms. In this paper, experimental method and comparative method are adopted to analyze content-based, user-based and hybrid recommendation algorithms, and attention mechanism and multi-task learning in deep learning are taken as improvement methods to improve the accuracy and effectiveness of personalized recommendation. Experimental results show that the hit rate of the improved algorithm is up to 0.79. The improved method presented in this paper achieves good performance on multiple data sets. © 2024 IEEE.

关键词： consensus algorithm

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Research on an Improved Weighted Average Passive Localization algorithm in Spherical Coordinates 4

Research on an Improved Weighted Average Passive Localizatio...

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4th International Conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Zhu, Yi-Xin Fu, Tian-Hui Hou, Wen-Da Lv, Hong Zhu, Yun-Han Naval University of Engineering School of Electrical and Electronic Engineering Wuhan China Troops 91001 Pla Beijing China Troops 92899 Pla Ningbo China Hunan University of Finance and Economics Accounting College Changsha China

ISBN: (纸本)9798331534622

This paper addresses the direction-finding problem in passive localization systems and proposes a novel algorithm for direction finding passive localization using an improved weighted average method in spherical coordinates. By constructing a suitable mathematical model and utilizing a weighted average algorithm for estimating the direction of received signals, the precision and reliability of localization are significantly enhanced. Experimental results demonstrate that this algorithm maintains higher stability and accuracy under conditions of large errors compared to traditional methods. © 2024 IEEE.

关键词： consensus algorithm

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Social Media Characteristic algorithm Based on Deep Network

Social Media Characteristic Algorithm Based on Deep Network

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Shangmingzhu, Jiang Shanghai Jian Qiao University 200336 China

ISBN: (纸本)9798350361537

In view of the limitations of traditional multidimensional analogy algorithms in the study of social media feature algorithms, a research scheme of media feature algorithm based on deep network was proposed. Firstly, the influencing factors is accurately located through gradient descent theory, and the indicators is reasonably divided to reduce interference, and the deep network is used to construct a research scheme for media feature algorithm. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional multi-dimensional analogy algorithm in terms of media feature science algorithm research accuracy and influencing factor processing time, and has obvious advantages. Media characterization algorithm research plays an extremely important role in social media, which can accurately predict and optimize the growth characteristics and product generation of social media. However, the traditional multi-dimensional analogy algorithm has certain limitations in solving the problem of feature study simulation, especially when dealing with complex problems. In this paper, this paper proposes a research scheme based on deep network media feature algorithm to better solve this problem. In this scheme, the influencing factors is accurately located through the gradient descent theory, so as to determine the division of indicators, and the deep network is used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the deep network-based simulation scheme can better solve the limitations of traditional multidimensional analogy algorithms and improve the accuracy and efficiency of simulation by using deep network simulation algorithm in the research of social media feature science algorithm. © 2024 IEEE.

关键词： consensus algorithm

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Randomized protocols for resilient peer-to-peer networks 19

Randomized protocols for resilient peer-to-peer networks

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Tanaka, Richard Zhu, Ying Ontario Tech University 2000 Simcoe Street North OshawaL1G 0C5 Canada

This paper studies a few randomized algorithms (e.g., random walks, gossip) in peer-to-peer networks. We leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and of these distributed randomized algorithms executing on top of them. We can thus analyze their behavior and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations. © 2024 The Authors. Published by Elsevier B.V.

关键词： consensus algorithm

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Pseudo-Deterministic Construction of Irreducible Polynomials over Finite Fields 44

Pseudo-Deterministic Construction of Irreducible Polynomials...

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44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2024

作者： Rai, Shanthanu S. Tata Institute of Fundamental Research Mumbai India

ISBN: (纸本)9783959773553

We present a polynomial-time pseudo-deterministic algorithm for constructing irreducible polynomial of degree d over finite field Fq. A pseudo-deterministic algorithm is allowed to use randomness, but with high probability it must output a canonical irreducible polynomial. Our construction runs in time Õ(d4 log4 q). Our construction extends Shoup's deterministic algorithm (FOCS 1988) for the same problem, which runs in time Õ (d4p1/2 log4 q) (where p is the characteristic of the field Fq). Shoup had shown a reduction from constructing irreducible polynomials to factoring polynomials over finite fields. We show that by using a fast randomized factoring algorithm, the above reduction yields an efficient pseudo-deterministic algorithm for constructing irreducible polynomials over finite fields. © 2024 Shanthanu S. Rai.

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