|
|||||
|
|
A class of improved conjugate gradient methods for nonconvex unconstrained optimization |
|
| Authors: | Qingjie Hu Hongrun Zhang Zhijuan Zhou Yu Chen |
| Institution: | 1. Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, China;2. Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin, China
School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China;3. School of Mathematics and Statistics, Guangxi Normal University, Guilin, China |
| Abstract: | In this paper, based on a new class of conjugate gradient methods which are proposed by Rivaie, Dai and Omer et al. we propose a class of improved conjugate gradient methods for nonconvex unconstrained optimization. Different from the above methods, our methods possess the following properties: (i) the search direction always satisfies the sufficient descent condition independent of any line search; (ii) these approaches are globally convergent with the standard Wolfe line search or standard Armijo line search without any convexity assumption. Moreover, our numerical results also demonstrated the efficiencies of the proposed methods. |
| Keywords: | Armijo line search global convergence nonconvex unconstrained optimization sufficient descent condition Wolfe line search |