A Two Term PRP Based Descent Method
首发时间:2006-12-15
Abstract:In this paper, by the use of the project of the PRP (Polak-Ribi秂re-Polyak) conjugate gradient direction, we develop a PRP based descent method for solving unconstrained optimization problem. The method provides a sufficiently descent direction for the objective function. Moreover, if exact line search is used, the method reduces to the standard PRP method. Under suitable conditions, we show that the method with some backtracking line search or the generalized Wolfe-type line search is globally convergent. We also report some numerical results and compare the performance of the method with some existing conjugate gradient methods. The results show that the proposed method is efficient.
keywords: unconstrained optimization, PRP method, global convergence
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一个两项下降的PRP方法
摘要:In this paper, by the use of the project of the PRP (Polak-Ribi秂re-Polyak) conjugate gradient direction, we develop a PRP based descent method for solving nconstrained optimization problem. The method provides a sufficiently descent direction for the objective function. Moreover, if exact line search is used, the method reduces to the standard PRP method. Under suitable conditions, we show that the method with some backtracking line search or the generalized Wolfe-type line search is globally convergent. We also report some numerical results and compare the performance of the method with some existing conjugate gradient methods. The results show that the proposed method is efficient.
关键词: unconstrained optimization, PRP method, global convergence
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No.1033889207116615****
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