王承竞 副教授
  • 学历:博士研究生毕业
  • 学位:理学博士学位
  • 办公地点:西南交通大学数学学院
  • 毕业院校:新加坡国立大学
  • 所在单位:数学学院
论文成果
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  • 发表刊物:IEEE Transactions on Signal Processing
  • 关键字:Nonconvex rank regression problems, semismooth Newton method, proximal-proximal majorization-minimization algorithm, proximal point algorithm
  • 摘要:In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex rank regression problems. The basic idea of the algorithm is to apply the proximal majorization-minimization algorithm to solve the nonconvex problem with the inner subproblems solved by a sparse semismooth Newton (SSN) method based proximal point algorithm (PPA). It deserves mentioning that we adopt the sequential regularization technique and design an implementable stopping criterion to overcome the singular difficulty of the inner subproblem. Especially for the stopping criterion, it plays a very important role for the success of the algorithm. Furthermore, we also prove that the PPMM algorithm converges to a stationary point. Due to the Kurdyka-Łojasiewicz (KL) property of the problem, we present the convergence rate of the PPMM algorithm. Numerical experiments demonstrate that our proposed algorithm outperforms the existing state-of-the-art algorithms.
  • 论文类型:SCI
  • 通讯作者:Peipei Tang, Chengjing Wang, Bo Jiang
  • 卷号:71
  • 页面范围:3502-3517
  • 是否译文:
  • 发表时间:2023-12-06
  • 收录刊物:SCI
  • 附件: Published_version_A_Proximal-Proximal_Majorization-Minimization_Algorithm_for_Nonconvex_Rank_Regression_Problems.pdf

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