Work

Eigenvalue Analysis of Hessian Matrices in Quadratic Loss Functions for Machine Learning Optimization

Year: 2025

Type: article

Abstract: Quadratic functions are fundamental to understand optimization landscapes in machine learning. In this study, we focus on the optimization of a class of quadratic loss functions, examining their behavior through the construction of Hessian matrices and the analysis of their eigenvalues.

Source: International journal of mathematics and computer science

DOI: 10.69793/ijmcs/03.2025/ramakrishna

Authors: A.V. Ramakrishna, T.V.N. Prasanna, N.N. Sairam

Institutions: Vignan's Foundation for Science, Technology & Research, Acharya Nagarjuna University

Language: English

Topic: Stochastic Gradient Optimization Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4410469666

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