Year: 2025
Type: article
Abstract: In recent years, deep learning technology has made breakthroughs in many fields. Its powerful data fitting ability and automatic feature extraction capability provide new approaches to solving traditional computational challenges. Deep learning models can automatically discover underlying patterns in large datasets through extensive learning and construct efficient mapping relationships. Therefore, applying deep learning techniques to the approximate solution of matrix eigenvalues holds promise for overcoming the limitations of traditional methods and offering an efficient and rapid solution to large-scale matrix eigenvalue problems. This paper will delve into the study of deep learning-based models for approximate matrix eigenvalue solutions, explore their potential applications in various scenarios, and verify their performance advantages through experiments.
Authors: Enyuan Wang, Ziqi Wang
Institution: Jiamusi University
Language: English
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412097479