A class of globally convergent three-term Dai-Liao conjugate gradient methods

摘要

Due to their simple structure and low memory requirements, conjugate gradient methods are widely used for solving large-scale optimization problems. In this paper, combining Dai-Liao conjugacy condition with a modified symmetric Perry matrix, a class of three-term Dai-Liao conjugate gradient algorithms are proposed. The given methods possess both Dai-Liao conjugacy condition and sufficient descent condition. Meanwhile, the global convergence of the presented algorithms are established under Wolfe line search for general objective functions. Numerical experiments show that the proposed methods are promising.