Graph Condensation for Large Factor Models

IF 0.5 4区数学 Q3 MATHEMATICS

Doklady Mathematics Pub Date : 2024-07-31 DOI:10.1134/s1064562424702090

B. N. Chetverushkin, V. A. Sudakov, Yu. P. Titov

引用次数: 0

Abstract

An original method for processing large factor models based on graph condensation using machine learning models and artificial neural networks is developed. The proposed mathematical apparatus can be used to plan and manage complex organizational and technical systems, to optimize large socioeconomic objects of national scale, and to solve problems of preserving the health of the nation (searching for compatibility of medications and optimizing health care resources).

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大系数模型的图形凝缩

摘要利用机器学习模型和人工神经网络，开发了一种基于图浓缩的处理大型因素模型的独创方法。所提出的数学装置可用于规划和管理复杂的组织和技术系统，优化国家规模的大型社会经济对象，以及解决维护国民健康的问题（寻找药物的兼容性和优化医疗资源）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Doklady Mathematics 数学-数学

CiteScore

1.00

自引率

16.70%

发文量

审稿时长

3-6 weeks

期刊介绍： Doklady Mathematics is a journal of the Presidium of the Russian Academy of Sciences. It contains English translations of papers published in Doklady Akademii Nauk (Proceedings of the Russian Academy of Sciences), which was founded in 1933 and is published 36 times a year. Doklady Mathematics includes the materials from the following areas: mathematics, mathematical physics, computer science, control theory, and computers. It publishes brief scientific reports on previously unpublished significant new research in mathematics and its applications. The main contributors to the journal are Members of the RAS, Corresponding Members of the RAS, and scientists from the former Soviet Union and other foreign countries. Among the contributors are the outstanding Russian mathematicians.