A COMPARATIVE ANALYSIS OF GRADIENT DESCENT AND LEAST SQUARES IN LINEAR REGRESSION

Abstract: This paper explores and compares the performance of Gradient Descent (GD) and the Least Squares (LS) method for solving linear regression problems. Theoretical aspects, computational efficiency, and accuracy are analyzed. While LS provides a closed-form solution, GD offers scalability advantages for large datasets. Empirical results demonstrate the trade-offs between these methods in terms of convergence rate, numerical stability, and computational cost.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-15
DOI
https://doi.org/10.5281/zenodo.15423647
Primary Topic
Stochastic Gradient Optimization Techniques
Type
article
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article

A COMPARATIVE ANALYSIS OF GRADIENT DESCENT AND LEAST SQUARES IN LINEAR REGRESSION

Zenodo (CERN European Organization for Nuclear Research)
Stochastic Gradient Optimization Techniques
article

A COMPARATIVE ANALYSIS OF GRADIENT DESCENT AND LEAST SQUARES IN LINEAR REGRESSION

article en

Abstract

Abstract: This paper explores and compares the performance of Gradient Descent (GD) and the Least Squares (LS) method for solving linear regression problems. Theoretical aspects, computational efficiency, and accuracy are analyzed. While LS provides a closed-form solution, GD offers scalability advantages for large datasets. Empirical results demonstrate the trade-offs between these methods in terms of convergence rate, numerical stability, and computational cost.

Zenodo (CERN European Organization for Nuclear Research)
Kent Hastanesi (TR)
Openalex Percentile: Top 75%
Stochastic Gradient Optimization Techniques
DOIOpen Access PDFOpenAlex
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A COMPARATIVE ANALYSIS OF GRADIENT DESCENT AND LEAST SQUARES IN LINEAR REGRESSION · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS