- Brief Biography
- Research
- Teaching
- Published Works

Professor Bingsheng He, started undergraduate study in February 1978, in the Department of Mathematics, Nanjing University. Upon graduation with Bachelor's degree, he received government sponsorship for oversea graduate study in Germany. He did his graduate study in University of Wuerzburg and earned doctor's degree in mathematics (1986). In 1987 he started to work in the faculty of Department of Mathematics, Nanjing University. He was promoted to professor title in 1997. Major awards Professor Bingsheng He has earned include: Jiangsu Province Mid –Age Expert with Outstanding Contributions; Winner of Jiangsu Provincial Science and Technology Progress Award; Recipient of Government Special Allowance of the Chinese State Council for Outstanding Academia. Long engaged in the research of optimization theory and methods, he studied the splitting methods for convex optimization and variational inequality in a simple unified framework, published more than 70 papers. Representative papers were published in Math. Progr., SIAM and other major academic publications. His main research results were introduced and cited at length by internationally renowned scholars. Most recently in October 2014, Professor He won the "Operations Research Award", a highly selective award issued by the Operations Research Society of China. He currently serves as a Professor in the Department of Mathematics at South University of Science and Technology of China.

◆ Optimization Theory and Methods, Linear and Nonlinear Programming, Mathematical Methods in Operations Research

◆ Numerical Methods for Monotone Variational Inequality, Splitting and Contraction Methods for Convex OPtimization

◆ 1987, Lecturer, Department of Mathematics, Nanjing University

◆ 1992, Associated Professor, Department of Mathematics, Nanjing University

◆ 1997, Professor, Department of Mathematics, Nanjing University

◆ 2013, Professor, School of Management Science and Engineering, Nanjing University

◆ 2015, Professor, Department of Mathematics, Southern University of Science and Technology

◆ 1963 ------ 1966， Nanjing High-school, Jiangsu Province, China

◆ 1978 ------ 1982， Nanjing University, China, Bachelor in Science

◆ 1983 ------ 1986， University of Wuerzburg, Germany Ph. D.

◆ 2000 ISI Citation Classic Award

◆ 2001 Winner of Jiangsu Provincial Science and Technology Progress Award, First Class

◆ 2002 Recipient of Governament Special Allowance of the Chinese State Counci

◆ 2003 Mid-Age Expert with Outstanding Contributions, Jiangsu Province

◆ 2014 Winner of Operations Research Award, Science and Technology, Society of Operation Research of China

◆ B.S. He, F. Ma and X.M. Yuan, Convergence Study on the Symmetric Version of ADMM with Larger Step Sizes, SIAM J. Imaging Science 9:1467-1501, 2016.

◆ B.S. He, H.K. Xu and X.M. Yuan, On the Proximal Jacobian Decomposition of ALM for Multiple-Block Separable Convex Minimization Problems and its Relationship to ADMM, J. Sci. Comput. 66 (2016) 1204-1217.

◆ 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.

◆ B.S. He and X.M. Yuan, On non-ergodic convergence rate of Douglas-Rachford alternating directions method of multipliers, Numerische Mathematik, 130: 567-577, 2015.

◆ B.S. He and X.M. Yuan, Block-wiseAlternating Direction Method of Multipliers for Multiple-block Convex Programming and Beyond, SMAI J. Computational Mathematics 1 (2015) 145-174.

◆ 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.

◆ B.S. He and X. M. Yuan, On the convergence rate of Douglas-Rachford operator splitting method, Mathematical Programming, 153 (2015) 715-722.

◆ E.X. Fang, B.S. He, H. Liu and X. M. Yuan, Generalized alternating direction method of multipliers: new theoretical insights and applications, Mathematical Programming Computation, 7 (2015) 149-187.

◆ B.S. He, M. Tao and X.M. Yuan, A splitting method for separable convex programming, IMA J. Numerical Analysis, 31: 394-426, 2015.

◆ B. S. He, Y. F. You and X. M. Yuan, On the Convergence of Primal-Dual Hybrid Gradient Algorithm, SIAM. J. Imaging Science 7: 2526-2537, 2014.

◆ B.S. He, H. Liu, Z.R. Wang and X. M. Yuan, A strictly Peaceman-Rachford splitting method for convex programming, SIAM J. Optim. 24: 1011-1040, 2014.

◆ G.Y. Gu, B.S. He and X.M. Yuan, Customized proximal point algorithms for linearly constrained convex minimization and saddle-point problems: a unified approach, Comput. Optim. Appl., 59: 135-161, 2014.

◆ X.J. Cai, G.Y. Gu and B.S. He, On the O(1/t) convergence rate of the projection and contraction methods for variational inequalities with Lipschitz continuous monotone operators, Comput. Optim. Appl., 57: 339-363, 2014

◆ B.S. He, X.M. Yuan and W.X. Zhang, A customized proximal point algorithm for convex minimization with linear constraints, Comput. Optim. Appl., 56:559-572, 2013.

◆ B.S. He and X.M. Yuan, Forward-backward-based descent methods for composite variational inequalities, Optimization Methods Softw. 28: 706-724, 2013.

◆ X.J. Cai, G.Y. Gu, B.S. He and X.M. Yuan, A proximal point algorithms revisit on the alternating direction method of multipliers, Science China Mathematics, 56 : 2179-2186, 2013.

◆ B.S. He, M. Tao and X.M. Yuan, Alternating Direction Method with Gaussian Back Substitution for Separable Convex Programming, SIAM J. Optim. 22: 313-340, 2012

◆ B.S. He and X.M. Yuan, On the $O(1/n)$ Convergence Rate of the Douglas-Rachford Alternating Direction Method，SIAM J. Numer. Anal. 50: 700-709, 2012.

◆ B.S. He and X.M. Yuan, Convergence analysis of primal-dual algorithms for a saddle-point problem: From contraction perspective, SIAM J. Imag. Sci., 5,:119-149, 2012.

◆ C.H. Chen, B.S. He, and X.M. Yuan, Matrix completion via alternating direction methods, IMA J. Numer. Anal., 32: 227-245, 2012.

◆ B.S. He, L.Z. Liao, and X. Wang, Proximal-like contraction methods for monotone variational inequalities in a unified framework I: Effective quadruplet and primary methods, Comput. Optim. Appl., 51: 649-679, 2012

◆ B.S. He, L.Z. Liao, and X. Wang, Proximal-like contraction methods for monotone variational inequalities in a unified framework II: General methods and numerical experiments, Comput. Optim. Appl. 51: 681-708, 2012

◆ B.S. He, M.H. Xu, and X.M. Yuan, Solving large-scale least squares covariance matrix problem by alternating direction methods, SIAM J. Matrix Anal. Appl., 32 ,136-152, 2011.

◆ B.S. He, L-Z Liao and S.L. Wang, Self-adaptive operator splitting methods for monotone variational inequalities, Numerische Mathematik, 94: 715-737, 2003

◆ B.S. He, L-Z Liao, D.R. Han and H. Yang, A new inexact alternating directions method for monotone variational inequalities, Mathematical Programming, 92: 103-118, 2002

◆ B.S He and L-Z Liao, Improvements of some projection methods for monotone nonlinear variational inequalities, J. Optimization Theory and Applications, 112: 111-128, 2002

◆ B.S. He, H. Yang and S.L. Wang, Alternating directions method with self-adaptive penalty parameters for monotone variational inequalities, Journal of Optimization Theory and applications, 106: 349-368, 2000.

◆ B.S. He and H. Yang, A neural network model for monotone asymmetric linear variational inequalities, IEEE Transactions on Neural Networks, 11: 3-16, 2000.

◆ B.S. He, Inexact implicit methods for monotone general variational inequalities, Mathematical Programming,86: 199-217, 1999.

◆ B.S. He, A class of projection and contraction methods for monotone variational inequalities, Appl. Math. Optimization, 35: 69-76, 1997.

◆ B.S. He, A new method for a class of linear variational inequalities, Mathematical Programming, 66: 137-144, 1994.

◆ B.S. He, Solving a class of linear projection equations, Numerische Mathematik, 68: 71-80, 1994.

◆ B.S. He and J. Stoer, Solution of projection problems over polytopes, Numerische Mathematik, 61: 73-90, 1992.

◆ B.S. He, A projection and contraction method for a class of linear complementarity problems and its application in convex quadratic programming, Applied Mathematics and Optimization, 25: 247-262, 1992.