A Scaled Gradient Modified Non-monotone Line Search Method for Constrained Optimization Problems
Optimization and Control
2026-05-01 v1 Functional Analysis
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.
Cite
@article{arxiv.2604.28110,
title = {A Scaled Gradient Modified Non-monotone Line Search Method for Constrained Optimization Problems},
author = {Qamrul Hasan Ansari and Feeroz Babu and D. R. Sahu and Jen Chih Yao},
journal= {arXiv preprint arXiv:2604.28110},
year = {2026}
}