Operations Research Transactions ›› 2025, Vol. 29 ›› Issue (4): 121-140.doi: 10.15960/j.cnki.issn.1007-6093.2025.04.011

• Research Article • Previous Articles     Next Articles

A second-order splitting method with its application

Zilin TAN1, Honglin LUO1,*()   

  1. 1. School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China
  • Received:2022-11-16 Online:2025-12-15 Published:2025-12-11
  • Contact: Honglin LUO E-mail:luohonglin@cqnu.edu.cn

Abstract:

Combining cubic regularization and trust region method to solve the subproblem, we propose a second-order splitting algorithm for a class of large scale separable nonconvex optimization problems under inexact Hessian information. The global convergence result is obtained under some mild assumptions. It is proved that the algorithm needs at most $O\left( {{\varepsilon ^{ - 2}}} \right) $ evaluations to produce a $\varepsilon $-approximate stable solution. The algorithm is employed to solve a nonconvex binary classification problem in machine learning with nice numerical experiments results.

Key words: large scale separable non-convex optimization problems, second-order splitting algorithm, inexact Hessian information, global convergence, complexity analysis, nonconvex binary classification

CLC Number: