Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{183994,
        author = {Dharminder Singh},
        title = {Comparative study of Smooth Penalty Function Algorithms for Solving Nonlinear Constrained Optimization Problems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {5},
        number = {3},
        pages = {388-394},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183994},
        abstract = {This paper undertakes a thorough comparative investigation of various smoothing techniques utilized in nonlinear optimization problems featuring inequality constraints. The objective of this comparative analysis is to elucidate the merits and limitations associated with each smoothing technique, offering insights into their applicability, convergence characteristics, and overall efficacy in the context of nonlinear inequality-constrained optimization problems. The findings of this study are intended to provide valuable guidance for researchers and practitioners engaged in the pursuit of optimal solutions, particularly in situations where conventional differentiability assumptions are not applicable.},
        keywords = {},
        month = {August},
        }