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| The nonlinear conjugate gradient methods utilize gradient information to search for the optimal solutions, and speed up the convergence rate by selecting conjugate search directions, which are characterized by fast convergence, low storage requirements, and wide applicability, as a result, it serves as an effective numerical method for solving nonlinear unconstrained optimization problems. In recent years, the nonlinear conjugate gradient methods have also been applied in the field of vector optimization. The focus of this paper is to introduce the research status and convergence results of some modified nonlinear conjugate gradient methods for scalar optimization, as well as the nonlinear conjugate gradient methods for vector optimization. |
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| Keywords:Vector optimization; Conjugate gradient methods; Unconstrained optimization; Pareto optimality; Inexact line search; Global convergence |
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