Convergence analysis of an infeasible quasi- Newton bundle method for nonsmooth convex programming

作     者：Jie SHEN Fangfang GUO Liping PANG Jie SHEN;Fangfang GUO;Liping PANG

作者机构：School of MathematicsLiaoning Normal UniversityDalian 116029China School of Mathematical SciencesDalian University of TechnologyDalian 116024China 

出 版 物：《Frontiers of Mathematics in China》 (中国高等学校学术文摘·数学（英文）)

年 卷 期：2023年第18卷第5期

页      面：367-380页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Non-smooth optimization convex constraint improvement function bundle method quasi-Newton direction 

摘      要：By utilizing the improvement function,we change the nonsmooth convex constrained optimization into an unconstrained optimization,and construct an infeasible quasi-Newton bundle method with proximal *** should be noted that the objective function being minimized in unconstrained optimization subproblem may vary along the iterations(it does not change if the null step is made,otherwise it is updated to a new function).It is necessary to make some adjustment in order to obtain the convergence *** employ the main idea of infeasible bundle method of Sagastizabal and Solodov,and under the circumstances that each iteration point may be infeasible for primal problem,we prove that each cluster point of the sequence generated by the proposed algorithm is the optimal solution to the original ***,for BFGS quasi-Newton algorithm with strong convex objective function,we obtain the condition which guarantees the boundedness of quasi-Newton matrices and the R-linear convergence of the iteration points.