A primal-dual algorithm framework for convex saddle-point optimization
作者: Benxin ZhangZhibin Zhu
作者单位: 1Guilin University of Electronic Technology
刊名: Journal of Inequalities and Applications, 2017, Vol.2017 (1)
来源数据库: Springer Journal
DOI: 10.1186/s13660-017-1548-z
关键词: Primal-dual methodProximal point algorithmConvex optimizationVariational inequalities
英文摘要: In this study, we introduce a primal-dual prediction-correction algorithm framework for convex optimization problems with known saddle-point structure. Our unified frame adds the proximal term with a positive definite weighting matrix. Moreover, different proximal parameters in the frame can derive some existing well-known algorithms and yield a class of new primal-dual schemes. We prove the convergence of the proposed frame from the perspective of proximal point algorithm-like contraction methods and variational inequalities approach. The convergence rate \(O(1/t)\) in the ergodic and nonergodic senses is also given, where t denotes the iteration number.
原始语种摘要: In this study, we introduce a primal-dual prediction-correction algorithm framework for convex optimization problems with known saddle-point structure. Our unified frame adds the proximal term with a positive definite weighting matrix. Moreover, different proximal parameters in the frame can derive some existing well-known algorithms and yield a class of new primal-dual schemes. We prove the convergence of the proposed frame from the perspective of proximal point algorithm-like contraction methods and variational inequalities approach. The convergence rate \(O(1/t)\) in the ergodic and nonergodic senses is also given, where t denotes the iteration number.
全文获取路径: Springer  (合作)
分享到:
来源刊物:
影响因子:0.822 (2012)

×
关键词翻译
关键词翻译
  • 最佳化 
  • framework 构架
  • saddle 
  • optimization 
  • point 
  • primal 最初
  • algorithm 算法
  • convex 凸起的
  • variational 变化的
  • existing 现行
  • iteration 迭代