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A globally convergent hybrid conjugate gradient method with strong Wolfe conditions for unconstrained optimization
Mathematical Sciences ( IF 2 ) Pub Date : 2019-11-05 , DOI: 10.1007/s40096-019-00310-y
P. Kaelo , P. Mtagulwa , M. V. Thuto

In this paper, we develop a new hybrid conjugate gradient method that inherits the features of the Liu and Storey (LS), Hestenes and Stiefel (HS), Dai and Yuan (DY) and Conjugate Descent (CD) conjugate gradient methods. The new method generates a descent direction independently of any line search and possesses good convergence properties under the strong Wolfe line search conditions. Numerical results show that the proposed method is robust and efficient.

中文翻译:

具有强Wolfe条件的全局收敛混合共轭梯度法无约束优化

在本文中,我们开发了一种新的混合共轭梯度方法,该方法继承了Liu and Storey(LS),Hestenes and Stiefel(HS),Dai and Yuan(DY)和Conjugate Descent(CD)共轭梯度方法的特征。新方法产生的下降方向与任何线搜索无关,并且在强Wolfe线搜索条件下具有良好的收敛性。数值结果表明,该方法是鲁棒有效的。
更新日期:2019-11-05
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