حیدر کشاورز

This paper presents a comprehensive unified framework that addresses the fundamental limitations of classical BFGS1 methods in unconstrained nonlinear optimization. We synergistically integrate Yang’s robust BFGS approach with Gill and Runnoe’s factored self- scaled BFGS methodology, resulting in the RFSS-BFGS2 algorithm.The proposed framework is rigorously evaluated through practical mobile localization based on ToA3 measurements Extensive numerical experiments demonstrate that RFSS-BFGS achieves superior performance with enhanced convergence reliability and accelerated convergence rates compared to state-of-the-art variants. The algorithm provides accurate mobile localization with significantly improved positioning accuracy and computational efficiency compared to conventional approaches.