Working Papers:     Back to Home


      1.  B.S. He, H. Liu, J.W. Lu and X.M. Yuan, Application ot the Strictly Contractive Peaceman-Rachford Splitting Method to

           Multi-block Separable Convex Optimization



      2.  B.S. He and X.M. Yuan, On the Direct Extension of ADMM for Multi-block Separable Convex Programming and Beyond:

           From Variational Inequality Perspective



       3. B.S. He, H.K. Xu  and X.M. Yuan, On the Proximal Jacobian Decomposition of ALM for Multi-block Separable Convex

           Minimization Problems and its Relationship to ADMM



        4 . 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 necessarily convergent.


      5.  B.S. He, L.S. Hou and X.M. Yuan, On full Jacobian decomposition of the  augmented Lagrangian method for separable convex



       6.  B.S. He and X.M. Yuan, On non-ergodic convergence rate of Douglas-Rachford alternating direction method of multipliers.

       7.  B.S. He and X.M. Yuan, On convergence rate of the Douglas-Rachford operator splitting method.

       8. G.Y. Gu, B.S. He, and X.M. Yuan, Customized proximal point algorithms for linearly constrained convex minimization and

           saddle-point problems: a uniform approach.


       9. B.S. He, and X.M. Yuan, On the O(1/t) convergence rate of alternating direction method.


     10. X.J. Cai, G.Y. Gu, B.S. He, and X.M. Yuan, A relaxed customized proximal point algorithm for seperable convex programming.


     11. B.S. He, On the O(1/t) convergence rate of the projection and contraction methods for variational inequalities with Lipschitz

           continuous monotone operators.


     12. B.S. He, and X.M. Yuan, A contraction method with implementable proximal regularization for linearly constrained convex programming.


     13. B.S. He, M. Tao, and X.M. Yuan, A splitting method for separate convex programming with linking linear constraints.