Two modified scaled nonlinear conjugate gradient methods

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:261

Article

Babaie-Kafaki, Saman

关键词: Unconstrained optimization;    Scaled nonlinear conjugate gradient method;    BFGS update;    Modified secant equation;    Sufficient descent condition;    Global convergence;   

DOI  :  10.1016/j.cam.2013.11.001

来源: Elsevier

【 摘 要 】

Following the scaled conjugate gradient methods proposed by Andrei, we hybridize the memoryless BFGS preconditioned conjugate gradient method suggested by Shanno and the spectral conjugate gradient method suggested by Birgin and Martinez based on a modified secant equation suggested by Yuan, and propose two modified scaled conjugate gradient methods. The interesting features of our methods are applying the function values in addition to the gradient values and satisfying the sufficient descent condition for the generated search directions which leads to the global convergence for uniformly convex functions. Numerical comparisons between the implementations of one of our methods which generates descent search directions for general functions and an efficient scaled conjugate gradient method proposed by Andrei are made on a set of unconstrained optimization test problems from the CUTEr collection, using the performance profile introduced by Dolan and More. (C) 2013 Elsevier B.V. All rights reserved.

【 授权许可】

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