摘要
In this paper, a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed, and the global convergence of this method with inexact line search is proved. Furthermore, we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method. The global convergence of this hybrid memoryless method is proved under mild assumptions. The initial results show that these new methods are efficient for the given test problems. Especially the memoryless non-quasi-Newton method requires little storage and computation, so it is able to efficiently solve large scale optimization problems.
In this paper,a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed,and the global convergence of this method with inexact line search is proved.Furthermore,we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method.The global convergence of this hybrid memoryless method is proved under mild assumptions.The initial results show that these new methods are effcient for the given test problems.Especially the memoryless non-quasi-Newton method requires little storage and computation,so it is able to effciently solve large scale optimization problems.
基金
Foundation item： the National Natural Science Foundation of China （No. 60472071）
the Science Foundation of Beijing Municipal Commission of Education （No. KM200710028001）.
