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| 1 | A proximal point algorithm revisit on the alternating direction method of multipliers显示文摘The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an application of the proximal point algorithm(PPA)to the dual problem of the model under consideration.This paper shows that ADMM can also be regarded as an application of PPA to the primal model with a customized choice of the proximal parameter.This primal illustration of ADMM is thus complemental to its dual illustration in the literature.This PPA revisit on ADMM from the primal perspective also enables us to recover the generalized ADMM proposed by Eckstein and Bertsekas easily.A worst-case O(1/t)convergence rate in ergodic sense is established for a slight extension of Eckstein and Bertsekas’s generalized ADMM. | CAI XingJu GU GuoYong HE BingSheng YUAN XiaoMing | 2013 | Science China Mathematics2013，56，10： | 20 |
| 2 | A projection and contraction method for a class of linear complementarity problems and its application in convex quadratic programming显示文摘 | Bingsheng He | 1992 | Applied Mathematics & Optimization1992，，3： | 2 |
| 3 | A Survey on Graph Processing Accelerators:Challenges and Opportunities显示文摘Graph is a well known data structure to represent the associated relationships in a variety of applications,e.g.,data science and machine learning.Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures,dedicated hardware solutions,also referred to as graph processing accelerators,are essential and emerging to provide the benefits significantly beyond what those pure software solutions can offer.In this paper,we conduct a systematical survey regarding the design and implementation of graph processing accelerators.Specifically,we review the relevant techniques in three core components toward a graph processing accelerator:preprocessing,parallel graph computation,and runtime scheduling.We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator.Interestingly,we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and the complexity of hardware configurations.We finally present and discuss several challenges in details,and further explore the opportunities for the future research. | Chuang-Yi Gui Long Zheng Bingsheng He Cheng Liu Xin-Yu Chen Xiao-Fei Liao Hai Jin | 2019 | Journal of Computer Science & Technology2019，34，2： | 2 |
| 4 | Mars: Accelerating MapReduce with Graphics Processors显示文摘 | Fang Wenbin He Bingsheng Luo Qiong | 2011 | IEEE Transactions on Parallel and Distributed Systems2011，22，4： | 1 |
| 5 | Inexact implicit methods for monotone general variational inequalities显示文摘 | Bingsheng He | 1999 | Mathematical Programming1999，，1： | 1 |
| 6 | A new approximate proximal point algorithm for maximal monotone operator显示文摘 | Bingsheng He Lizhi Liao Zhenhua Yang | 2003 | Science in China Series A: Mathematics2003，，2： | 1 |
| 7 | A new inexact alternating directions method for monotone variational inequalities显示文摘 | Bingsheng He Li-Zhi Liao Deren Han Hai Yang | 2002 | Mathematical Programming2002，，1： | 1 |
| 8 | Relational query coprocessing on graphics processors显示文摘 | He Bingsheng Lu Mian Yang Ke | 2009 | ACM Trans on Database Syst2009，34，4： | 1 |
| 9 | A predict-correct projection method for monotone variant variational inequalities显示文摘A predict_correct projection method is presented for solving monotone variant variational inequalities, which could exploit the advantages and overcome the difficulties of both explicit and implicit projection methods. | HAN Qiaoming 1 and HE Bingsheng 2 1. Institute of Applied Mathematics, Chinese Academy of Sciences, Beijing 100080, China 2. Department of Mathematics, Nanjing University, Nanjing 210093, China | 1998 | Chinese Science Bulletin1998，43，15： | 1 |
| 10 | Sander: relational query coprocessing on graphics processors显示文摘 | He Bingsheng Lu Mian Yang Ke | 2009 | ACM Trans on Database Syst2009，34，4： | 1 |
| 11 | A class of projection and contraction methods for monotone variational inequalities显示文摘 | He Bingsheng | 1997 | Ap- plied Mathematics and Optimization1997，35，1： | 1 |
| 12 | Revisiting co-processing for hash joins on the coupled CPU-GPU architecture显示文摘 | He Jiong Lu Mian He Bingsheng | 2013 | PVLDB2013，6，10： | 1 |
| 13 | Parallel splitting augmented Lagrangian methods for monotone structure varialtion inequality显示文摘 | HE Bingsheng | | 0，，2： | 1 |
| 14 | Improving main memory hash joins on Intel Xeon Phi processors: an experimental approach显示文摘 | Jha S He Bingsheng Lu Mian | 2015 | PVLDB2015，8，6： | 1 |
| 15 | A class of projection and contraction methods for monotone variational inequalities显示文摘 | Bingsheng He | 1997 | Applied Mathematics & Optimization1997，，1： | 1 |
| 16 | A new method for a class of linear variational inequalities显示文摘 | Bingsheng He | 1994 | Mathematical Programming (-)1994，，1： | 1 |
| 17 | Inexact implicit methods for monotone general variational inequalities显示文摘 | Bingsheng He | 1999 | Mathematical Programming1999，，1： | 1 |
| 18 | A new method for a class of linear variational inequalities显示文摘 | Bingsheng He | 1994 | Mathematical Programming (-)1994，，1： | 1 |
| 19 | A projection and contraction method for a class of linear complementarity problems and its application in convex quadratic programming显示文摘 | Bingsheng He | 1992 | Applied Mathematics & Optimization1992，，3： | 1 |
| 20 | A projection and contraction method for a class of linear complementarity problems and its application in convex quadratic programming显示文摘 | Bingsheng He | 1992 | Applied Mathematics & Optimization1992，，3： | 1 |
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