On the Extension of Dai-Liao Conjugate Gradient Method for Vector Optimization

In this paper, we extend the Dai-Liao conjugate gradient method to vector optimization. Firstly, we analyze the global convergence of the direct extension version of the Dai-Liao conjugate gradient method for K-strongly convex vector functions. Secondly, we investigate the global convergence of the vector version of restricted non-negative Dai-Liao conjugate gradient method for general vector functions. Additionally, we discuss the global convergence of the vector version of modified Dai-Liao conjugate gradient method for general vector functions. Finally, numerical experiments demonstrate that the proposed conjugate gradient methods are effective for solving vector optimization problems. In particular, these methods can effectively generate the Pareto frontiers for the test problems.