A NEW ADAPTIVE SUBSPACE MINIMIZATION THREE-TERM CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

Keke Zhang1, Hongwei Liu1, Zexian Liu2

1. 1. School of Mathematics and Statistics, Xidian University, Xi'an 710126, China;
2. State Key Laboratory of Scientific and Engineering Computing, Institute of Computational Mathematics and Scientific/Engineering computing, AMSS, Chinese Academy of Sciences, Beijing 100190, China
• Contact: Zexian Liu,Email:liuzexian2008@163.com
• Supported by:
We would like to thank the editor and the anonymous referees for their valuable comments, which are helpful to improve the quality of this paper. We would like to thank Professor Dai Y.H. and Dr. Kou C.X. for their CGOPT code, and thank Professors Hager W.W. and Zhang H.C. for their CG DESCENT(5.3) code. This research is supported by National Science Foundation of China (No. 11901561), China Postdoctoral Science Foundation (2019M660833) and Guangxi Natural Science Foundation (No. 2018GXNSFBA281180).

Keke Zhang, Hongwei Liu, Zexian Liu. A NEW ADAPTIVE SUBSPACE MINIMIZATION THREE-TERM CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION[J]. Journal of Computational Mathematics, 2021, 39(2): 159-177.

A new adaptive subspace minimization three-term conjugate gradient algorithm with nonmonotone line search is introduced and analyzed in this paper. The search directions are computed by minimizing a quadratic approximation of the objective function on special subspaces, and we also proposed an adaptive rule for choosing different searching directions at each iteration. We obtain a significant conclusion that the each choice of the search directions satisfies the sufficient descent condition. With the used nonmonotone line search, we prove that the new algorithm is globally convergent for general nonlinear functions under some mild assumptions. Numerical experiments show that the proposed algorithm is promising for the given test problem set.

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