Subspace Minimization Conjugate Gradient Method Based on Cubic Regularization Model for Unconstrained Optimization

作     者：Ting Zhao Hongwei Liu Ting Zhao;Hongwei Liu

作者机构：School of Mathematics and StatisticsXidian UniversityXi'an 710126China 

出 版 物：《Journal of Harbin Institute of Technology(New Series)》 (哈尔滨工业大学学报（英文版）)

年 卷 期：2021年第28卷第5期

页      面：61-69页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070105[理学-运筹学与控制论] 0701[理学-数学] 

基　　金：Sponsored by the National Natural Science Foundation of China(Grant No.11901561) 

主　　题：cubic regularization model conjugate gradient method subspace technique unconstrained optimization 

摘      要：Many methods have been put forward to solve unconstrained optimization problems,among which conjugate gradient method(CG)is very *** the increasing emergence of large⁃scale problems,the subspace technology has become particularly important and widely used in the field of *** this study,a new CG method was put forward,which combined subspace technology and a cubic regularization ***,a special scaled norm in a cubic regularization model was *** certain conditions,some significant characteristics of the search direction were given and the convergence of the algorithm was *** comparisons show that for the 145 test functions under the CUTEr library,the proposed method is better than two classical CG methods and two new subspaces conjugate gradient methods.