DEEP NEURAL NETWORK-BASED APPROACH FOR COMPUTING SINGULAR VALUES OF MATRICES

Matrix factorization techniques, such as Singular Value Decomposition (SVD), Eigenvalue Decomposition (EVD), and QR

decomposition, have long been pivotal in computational mathematics, particularly for applications in signal processing,

machine learning, and data analysis. With the growing size and complexity of data, traditional methods of matrix

factorization face challenges in efficiency and scalability. This paper investigates the implementation of Convolutional

Neural Networks (CNNs) for computing the singular values of both real and complex matrices. By leveraging the

hierarchical feature extraction capabilities of CNNs, this approach aims to enhance the accuracy, efficiency, and scalability

of SVD calculations. The proposed CNN-based SVD method is evaluated against the conventional SVD algorithm,

demonstrating superior performance in terms of computational time and accuracy.