Shujie Jing

Work place: School of Mathematics and Information Science, Henan Polytechnic University, Henan, China

E-mail: jsj-jjj@hpu.edu.cn

Website:

Research Interests: Computational Complexity Theory, Theory of Computation, Numerical Analysis

Biography

Shujie Jing received a master of Science Degree in Computational Mathematics from Xi'an Jiaotong University in 1997. He is currently a lecture in the School of Mathematics and Information Science at Henan Polytechnic University, China. His research interests include optimization theory, geometric programming, convex analysis, numerical solution of nonlinear equations.

Author Articles
A Hybrid Spectral Conjugate Gradient Method with Global Convergence

By Jing Liu Shujie Jing

DOI: https://doi.org/10.5815/ijmsc.2022.02.01, Pub. Date: 8 Jun. 2022

The spectral conjugate gradient (SCG) method is one of the most commonly used methods to solve large- scale nonlinear unconstrained optimization problems. It is also the research and application hot spot of optimization theorists and optimization practitioners. In this paper, a new hybrid spectral conjugate gradient method is proposed based on the classical nonlinear spectral conjugate gradient method. A new parameter  is given. Under the usual assumptions, the descending direction independent of any line search is generated, and it has good convergence performance under the strong Wolfe line  search condition . On a set of test problems, the numerical results show that the algorithm is effective.

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