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A Scaled Gradient Modified Non-monotone Line Search Method for Constrained Optimization Problems

Year: 2026

Type: preprint

Abstract: In this paper, we propose a scaled gradient modified non-monotone line search method for solving constrained minimization problems, and explore several specific properties of this method, namely, its convergence analysis. We discuss the linear convergence rate of the sequence generated by the proposed algorithm to a solution of the constrained minimization problem where the objective function is strongly quasiconvex. We consider numerical examples of large-scale fractional programming and quadratic programming for the function of pseudo convex and strongly quasiconvex and compare the performance of the proposed algorithm with the existing ones for these examples.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2604.28110

Authors: Ansari, Qamrul Hasan, Babu, Feeroz, Sahu, D. R., Yao, Jen Chih

Institutions: King Fahd University of Petroleum and Minerals, China Medical University

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: China Medical University

OpenAlex ID: W7159598218

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