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Dynamic Parameterized Feedback Problems in Tournaments 19

Dynamic Parameterized Feedback Problems in Tournaments

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

作者： Zych-Pawlewicz, Anna Żochowski, Marek Institute of Informatics University of Warsaw Poland

ISBN: (纸本)9783959773539

In this paper we present the first dynamic algorithms for the problem of K-Feedback Arc Set in Tournaments (K-Fast) and the problem of K-Feedback Vertex Set in Tournaments (K-Fvst). Our algorithms maintain a dynamic tournament on n vertices altered by redirecting the arcs, and answer if the tournament admits a feedback arc set (or respectively feedback vertex set) of size at most K, for some chosen parameter K. For dynamic K-Fast we offer two algorithms. In the promise model, where we are guaranteed, that the size of the solution does not exceed g(K) for some computable function g, we give an O(pg(K)) update and O(3KK√K) query algorithm. In the general setting without any promise, we offer an O(log2 n) update and O(3KK log2 n) query time algorithm for dynamic K-Fast. For dynamic K-Fvst we offer an algorithm working in the promise model, which admits O(g5(K)) update and O(3KK3g(K)) query time. © Anna Zych-Pawlewicz and Marek Żochowski.

关键词： consensus algorithm

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MAGNOLIA: Matching algorithms via GNNs for Online Value-to-go Approximation 41

MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-g...

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41st International Conference on Machine Learning, ICML 2024

作者： Hayderi, Alexandre Saberi, Amin Vitercik, Ellen Wikum, Anders Department of Computer Science Stanford University StanfordCA United States Department of Management Science & Engineering Stanford University StanfordCA United States

Online Bayesian bipartite matching is a central problem in digital marketplaces and exchanges, including advertising, crowdsourcing, ridesharing, and kidney exchange. We introduce a graph neural network (GNN) approach that emulates the problem's combinatorially-complex optimal online algorithm, which selects actions (e.g., which nodes to match) by computing each action's value-to-go (VTG)-the expected weight of the final matching if the algorithm takes that action, then acts optimally in the future. We train a GNN to estimate VTG and show empirically that this GNN returns high-weight matchings across a variety of tasks. Moreover, we identify a common family of graph distributions in spatial crowdsourcing applications, such as rideshare, under which VTG can be efficiently approximated by aggregating information within local neighborhoods in the graphs. This structure matches the local behavior of GNNs, providing theoretical justification for our approach. Copyright 2024 by the author(s)

关键词： consensus algorithm

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Classification algorithms with a Complex Structure in Pattern Recognition 25

Classification Algorithms with a Complex Structure in Patter...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Nishanov, Akram Kh. Allamov, Oybek Rakhmanov, Askar T. Yusupova, Janar Ruzibaev, Ortik B. Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Software Engineering Urgench Branch of Tashkent University of Information Technology named after Muhammad al Khwarizmi Urgench Uzbekistan Department of Systematic and Practical Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Faculty of Software Engineering Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9798350389234

The article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed. © 2024 IEEE.

关键词： consensus algorithm

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Contextual Bandit algorithm with Decision Trees and Upper Confidence Bound for Adaptive Recommendation 51

Contextual Bandit Algorithm with Decision Trees and Upper Co...

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51st International Conference on Computers and Industrial Engineering, CIE 2024

作者： Oiwa, Sho Abe, Taichi Kimura, Keigo Suzuki, Satoshi Goto, Masayuki Waseda University Graduate School of Creative Science and Engineering Okubo 3-4-1 Shinjuku-ku Tokyo169-8555 Japan Waseda University School of Creative Science and Engineering Okubo 3-4-1 Shinjuku-ku Tokyo169-8555 Japan

The Contextual Bandit algorithm, an online recommender system, makes recommendations and learns user characteristics simultaneously based on context such as user attributes. The method balances exploration and exploitation to maximize accumulated reward. Specifically, exploration involves recommending a variety of products, while exploitation involves recommending products with high expected rewards. To accurately estimate rewards from context, it is essential to adopt an appropriate model for the given situations. Additionally, it should be noted that the relationship between context and reward is often non-linear. TreeBootstrap, which uses decision trees to estimate rewards, has been proposed as a method suitable for such situations. However, the model may have a different structure even when trained on the same data because TreeBootstrap balances exploration and exploitation by bootstrapping the training data. It may not be able to fully utilize the information obtained through exploration, which may lead to insufficient cumulative reward. In this study, we propose TreeUCB, which balances exploration and exploitation by using a confidence upper bound on the expected reward instead of bootstrapping the training data. The confidence upper bound is derived from the assumption that the number of data points with a reward of 1 in a leaf node follows a binomial distribution. We conduct experiments on artificial data to demonstrate the effectiveness of the proposed method, including other methods suitable for situations where there is a non-linear relationship between context and reward. In addition, we discuss the balance between exploration and exploitation by comparing the reward distributions of each method and several case studies. © 2024 Computers and Industrial Engineering. All rights reserved.

关键词： consensus algorithm

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Computer's Error Rate Improvement by using Quantity Computational algorithm Comparing with Grover's algorithm

Computer's Error Rate Improvement by using Quantity Computat...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, K. Ajay Reena, B. Ardly Melba Karthick, V. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350373783

In order to achieve the primary purpose of this research, which is to improve the error rate of quantum computers through the utilization of quantum algorithms. The Components and Procedures: For the purpose of this study, two groups are selected, and a total of twenty samples are taken into consideration. It is generally agreed that the Quantity Computational algorithm belongs to group 1, while Grover's algorithm is regarded to be part of group 2. A total of ten samples are included in both Group 1 and Group 2, which likewise includes ten samples. The value of alpha is 0.05, the value of beta is 0.2, and the quality of the G power is 80%. The results show that the mistake rate of the Quantity computational algorithm is higher than the error rate of Grover's approach. With a mean error rate detection of ±2 standard deviations, these findings indicated that there was no statistically significant difference between the two groups. The p value of 0.02 indicates that the hypothesis is absolute, and the test was conducted using an independent sample T test. In light of this, it can be concluded that the mistake rate of the Quantity computational algorithm is higher than the error rate of the Grover's technique © 2024 IEEE.

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Construction of Intelligent Evaluation System for English Writing Based on iWrite System and BP algorithm

Construction of Intelligent Evaluation System for English Wr...

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作者： Jiang, Hua Shankar, Smitha Sharath Teaching and Research Institute of Foreign Languages Bohai University Liaoning Jinzhou China Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

BP neural network algorithm has the advantages of strong learning ability, strong adaptability, and good fault tolerance, which makes up for the shortcomings of traditional intelligent algorithms, and is widely used in various evaluation systems. The iWrite intelligent review engine integrates the latest achievements in deep learning fields such as neural machine translation and transfer learning. The combination of the iWrite system and the BP algorithm brings into play the routine evaluation function of the iWrite system, exerts the learning ability and knowledge discovery ability of the BP algorithm, and promotes the evaluation of college English writing into the era of intelligence. This paper applies the latest technologies in the fields of corpus linguistics and natural language processing, adopts a comprehensive composition evaluation concept, and studies the English composition evaluation method combining iWrite system and BP algorithm, so as to promote teaching evaluation into the era of intelligence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

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Distributed Connected Dominating Set Based algorithm for Mobile Ad-Hoc Networks 11

Distributed Connected Dominating Set Based Algorithm for Mob...

引用

11th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Yilmaz, Aziz Can Erdaş, Çaǧatay Berke Başkent University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362541

Ad-hoc networks are a communication protocol used in various sectors, particularly in military technologies. Unlike traditional network structures, Ad-hoc networks operate on the principle of peer-to-peer communication. Since the nodes within the network are not connected to a cellular infrastructure, distributed algorithms are required for the communication of nodes. However, it is important to carefully design these distributed algorithms based on essential criteria due to the challenges associated with ad-hoc networks. These criterias are considered to be particularly important when dealing with dynamically variable networks, such as a mobile ad-hoc networks. Mobile ad-hoc networks face significant challenges related to scaling, energy consumption, and stability. To address these challenges, various clustering algorithms employed, each with its own benefits and costs. In this study, a connected dominating set algorithm is proposed for mobile ad hoc networks, which can provide workload balance and protect the backbone structure relatively against mobility and can be an alternative solution to the congestion occurring on a single backbone by creating multiple backbone structures. © 2024 IEEE.

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A Distributed algorithm for Solving Time-Varying Linear Equations based on the Newton Methods 3

A Distributed Algorithm for Solving Time-Varying Linear Equa...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Shao, Jiayi Yu, Hao Shi, Dawei School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331540319

This study introduces a distributed algorithm based on the Newton methods, which is designed to collaboratively solve time-varying linear equations with a unique solution through the cooperation of multiple agents. Each agent possesses only a portion of the global equation's information, specifically, a subset of the rows of the augmented matrix. Through communication among agents, the algorithm converges towards the global solution. When the derivative of the global solution of the time-varying equations is bounded, the algorithm ensures that the local solutions converge within a certain range of the global solution, thereby achieving tracking. The algorithm is also capable of rapidly solving time-invariant linear equations, quickly reaching consensus and determining the global solution of the equations. Efficacy and efficiency of the algorithm have been substantiated through a simulation experiment. © 2024 IEEE.

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Distributed Policy Evaluation with Local Updates over Time-Varying Communication Network 59

Distributed Policy Evaluation with Local Updates over Time-V...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Crespo, Matthew Enyioha, Chinwendu University of Central Florida Electrical and Computer Engineering Orlando United States

ISBN: (纸本)9798331513269

This paper studies a consensus-based policy evaluation algorithm in a cooperative team of heterogeneous learners. To improve each agent's approximation of their value function, they each update their weight parameters, then perform consensus updates with neighboring agents over a dynamic communication network. We present the analysis of an efficient fully distributed algorithm for cooperatively evaluating and optimizing their value functions over a time-varying communication network in the average-reward setting. Our main result shows that, using this algorithm, the agents can approximate the true value function, with the approximation error dependent on the number of communication rounds and number of time steps it takes for the communication network to be connected. To validate the theoretical results, we present accompanying numerical experiments that show the theoretical error bounds are not only tight, but get tighter as the time steps required to guarantee network connectivity reduces. © 2025 IEEE.

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Application of the Garcia-Wachs algorithm to Build a Minimum Cost Search Tree

Application of the Garcia-Wachs Algorithm to Build a Minimum...

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2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Gadasin, D.V. Palibza, S.A. Tremasova, L.A. Zimnin, A.S. Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

The attribute values of a certain subject area can be ordered according to the probability of their occurrence, and based on them it is possible to build a tree on which to implement a search algorithm and determine whether the search data is valid for the selected attribute. One of the possible structures of the domain representation is a tree, and the search for the necessary attribute can take place on the search tree. In a binary search tree, any internal vertex is connected by two links, either with internal vertices, or with external vertices, or with internal and external ones. The key characteristic of the tree is the cost of the tree, which corresponds to the average length of the path from the root vertex to the outer one. One of the algorithms for building a minimum cost search tree is the Garcia-Wachs algorithm. This algorithm builds an optimal binary tree based on a predefined order. The paper considers various initial states and determines the most suitable ones. © 2025 IEEE.

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