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      多个可分离函数凸优化问题的ADMM类方法论文介绍 

 

     1. B.S. He, Parallel splitting augmented Lagrangian methods for monotone structured variational inequalities

         Computational Optimization and Applications 42 (2009), 195-212.

      2. B.S. He, M. Tao and X.M. Yuan, Alternating Direction Method with Gaussian Back substitution for separable convex programming,

          SIAM J. Optim. 22(2012), 313-340.

     3. G.Y. Gu, B.S. He and  J.F. Yang, Inexact Alternating-Direction-Based Contraction Methods for Separable Linearly Constrained convex optimization

           JOTA 163 (2014) 105-129.

     4.  B.S. He, M. Tao and X.M. Yuan, A splitting method for separable convex programming,

          IMA J. Numerical  Analysis, 31(2015), 394-426.

     5.  B.S. He, L.S. Hou, and X.M. Yuan, On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable  Convex Programming,

          SIAM J. Optim., 25 (2015) 2274–2312.

     6. B.S. He, M. Tao and X. M. Yuan, Convergence rate analysis for the alternating direction method of multipliers with a substitution procedure

          for separable convex programming, Mathematics of Operations Research, 42 (2017) 662-691.

     7. C.H. Chen, B.S. He, Y.Y. Ye and X. M. Yuan, The direct extension of ADMM for multi-block convex minimization problems is not necessary convergent,

         Mathematical Programming, 155 (2016) 57-79.

     8. B.S. He, and X. M. Yuan, A class of ADMM-based algorithms for three-block separable convex programming.   

         Computational Optimization and Applications 702018791–826.

 

Last Update: Sept. 30, 2019