A Mixed Conjugate Gradient Method for Unconstrained Optimization Problem
- Resource Type
- Conference
- Authors
- Qiao, B.; Yang, L.; Liu, J.; Yao, Y.
- Source
- 2017 13th International Conference on Computational Intelligence and Security (CIS) CIS Computational Intelligence and Security (CIS), 2017 13th International Conference on. :520-523 Dec, 2017
- Subject
- Computing and Processing
Convergence
Gradient methods
Numerical simulation
Linear programming
Algorithm design and analysis
unconstrained optimization
global convergence
mixed conjugate gradient
- Language
In this paper, we propose a mixed conjugate gradient method for unconstrained optimization problem based on the HS method and DY method. The new method has taken advantages of two methods. The global convergence of the mixed conjugate gradient method is proved under the Wolfe line search which is no need for the descent condition. The numerical experimental results on some classical problems show that the new method is efficient.