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.
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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