{"title":"利用神经网络进行大规模特征值计算的分布式合作人工智能","authors":"Ronald Katende","doi":"arxiv-2409.06746","DOIUrl":null,"url":null,"abstract":"This paper presents a novel method for eigenvalue computation using a\ndistributed cooperative neural network framework. Unlike traditional techniques\nthat struggle with scalability in large systems, our decentralized algorithm\nenables multiple autonomous agents to collaboratively estimate the smallest\neigenvalue of large matrices. Each agent uses a localized neural network model,\nrefining its estimates through inter-agent communication. Our approach\nguarantees convergence to the true eigenvalue, even with communication failures\nor network disruptions. Theoretical analysis confirms the robustness and\naccuracy of the method, while empirical results demonstrate its better\nperformance compared to some traditional centralized algorithms","PeriodicalId":501162,"journal":{"name":"arXiv - MATH - Numerical Analysis","volume":"50 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distributed Cooperative AI for Large-Scale Eigenvalue Computations Using Neural Networks\",\"authors\":\"Ronald Katende\",\"doi\":\"arxiv-2409.06746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel method for eigenvalue computation using a\\ndistributed cooperative neural network framework. Unlike traditional techniques\\nthat struggle with scalability in large systems, our decentralized algorithm\\nenables multiple autonomous agents to collaboratively estimate the smallest\\neigenvalue of large matrices. Each agent uses a localized neural network model,\\nrefining its estimates through inter-agent communication. Our approach\\nguarantees convergence to the true eigenvalue, even with communication failures\\nor network disruptions. Theoretical analysis confirms the robustness and\\naccuracy of the method, while empirical results demonstrate its better\\nperformance compared to some traditional centralized algorithms\",\"PeriodicalId\":501162,\"journal\":{\"name\":\"arXiv - MATH - Numerical Analysis\",\"volume\":\"50 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - MATH - Numerical Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.06746\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - MATH - Numerical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Distributed Cooperative AI for Large-Scale Eigenvalue Computations Using Neural Networks
This paper presents a novel method for eigenvalue computation using a
distributed cooperative neural network framework. Unlike traditional techniques
that struggle with scalability in large systems, our decentralized algorithm
enables multiple autonomous agents to collaboratively estimate the smallest
eigenvalue of large matrices. Each agent uses a localized neural network model,
refining its estimates through inter-agent communication. Our approach
guarantees convergence to the true eigenvalue, even with communication failures
or network disruptions. Theoretical analysis confirms the robustness and
accuracy of the method, while empirical results demonstrate its better
performance compared to some traditional centralized algorithms