Um algoritmo de busca linear para otimização irrestrita

Abstract

This work presents a linear search algorithm for unconstrained optimization problems proposed by Gonglin Yuan, Sha Lu Wei and Zengxi [1], called here by Algorithm GSZ. This algorithm is designed from the perspective of inheriting the simplicity and low computational cost of the conjugate gradient method. n this context, a detailed proof of the global convergence analysis for functions not necessarily convex is presented. We also emphasize the achievement of the linear convergence rate for the case where the function is strongly Convex.