Welcome to Defeng Sun's Home Page

Defeng SUN (孫德鋒)

Chair Professor

Department of Applied Mathematics

The Hong Kong Polytechnic University

Hong Kong

Brief History
Research Interests
Teaching
Recruitments
Professional Activities

Recognitions
Codes

Some Recent Talks
Some Old Talks
Publications

 

SUN Defeng

Department of Applied Mathematics
The Hong Kong Polytechnic University 
Hung Hom, Kowloon, Hong Kong

 

Office: TU 728, Yip Kit Chuen Building

Phone: +852 2766 6935

Fax: +852 2362 9045

Email: defeng.sun@polyu.edu.hk

Web: https://www.polyu.edu.hk/ama/profile/dfsun

Brief History

Born in a small village (where the story of Mo Yan’s Nobel prize winning novel Red Sorghum took place) located at Gaomi County (高密), Shandong Province, China. BSc (1989) from Nanjing University, China, majoring in Computational Mathematics; MSc (1992) also from Nanjing University, working on Variational Inequalities under the supervision of Professor Bingsheng He and Stochastic Optimization under the supervision of Professor Jinde Wang; PhD (1995) from Institute of Applied Mathematics, Chinese Academy of Sciences under the supervision of Professor Jiye Han focusing on Nonsmooth Equations and Optimization; Visiting Fellow, Research Associate and then Australian Postdoctoral Fellow, the University of New South Wales, Australia (1995--2000) all working in the area of Optimization; Assistant Professor (December 2000--December  2005)/Associate Professor (January 2006--June 2009)/Professor (July 2009--) at Department of Mathematics, National University of Singapore. I also worked for Risk Management Institute (RMI) as Deputy Director, Research (August 2009--August 2014) and its acting program director to Masters of Financial Engineering (March--June, 2014).  I joined The Hong Kong Polytechnic University on August 1, 2017 as Chair Professor of Applied Optimization and Operations Research at Department of Applied Mathematics.

Recent Research Interests

Teaching

Recruitments

Professional Activities

Recognitions

Codes in Matlab and others

Codes for nearest (covariance) correlation matrix problems

Codes under the Matrix Optimization (MatOpt) Project

[Defeng Sun, Kim Chuan Toh, Yancheng Yuan, Xin-Yuan Zhao, SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0), to appear in Optimization Methods and Software (2019).]

[Liuqin Yang, Defeng Sun, and Kim Chuan Toh, SDPNAL+: a majorized semismooth Newton-CG augmented Lagrangian method for semidefinite programming with nonnegative constraints, Mathematical Programming Computation, 7 (2015), pp. 331-366.]

[Defeng Sun, Kim Chuan Toh, and Liuqin Yang, “A convergent 3-block semi-proximal alternating direction method of multipliers for conic programming with 4-type constraints”, SIAM Journal on Optimization Vol. 25, No. 2 (2015) 882–915. Detailed computational results for over 400 problems tested in the paper. You may also find a supplementary note here on more detailed comparisons between the performance of our proposed algorithm and various variants of ADMMs.]

[X.Y. Zhao, D.F. Sun, and Kim Chuan Toh, A Newton-CG augmented Lagrangian method for semidefinite programming, SIAM Journal on Optimization, 20 (2010), pp. 1737--1765.]

 

Codes under the Statistical Optimization (StaOpt) Project

 

Codes for rank constrained problems

Codes for other problems

Some recent talks

Some old talks

Selected Publications

Click here for my google scholar page.

Click here for my ORCID page.

Technical Reports

 Click here for the arXived

 

 

 

2020--

·         Liang Chen, Xudong Li, Defeng Sun, and Kim Chuan Toh, On the Equivalence of   Inexact Proximal ALM and ADMM for a Class of Convex Composite Programming”, Mathematical Programming 18X (2020) [DOI:10.1007/s10107-019-01423-x] https://arxiv.org/pdf/1803.10803.pdf

·         Xudong Li, Defeng Sun, and Kim Chuan Toh,On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope”, Mathematical Programming 180 (2020) 1--28 [DOI: 10.1007/s10107-018-1342-9] https://arxiv.org/abs/1702.05934

·         Yangjing Zhang, Ning Zhang, Defeng Sun, and Kim Chuan Toh, An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems”,   Mathematical Programming 179 (2020) 1--41 [DOI:10.1007/s10107-018-1329-6] https://arxiv.org/pdf/1712.05910.pdf

·         Defeng Sun, Kim Chuan Toh, Yancheng Yuan, Xin-Yuan Zhao, “SDPNAL+: A Matlab software for semidefinite programming with bound constraints (version 1.0)”, Optimization Methods and Software 35 (2020) 1--30. [https://doi.org/10.1080/10556788.2019.1576176] https://arxiv.org/pdf/1710.10604.pdf

2019

·         Ziyan Luo, Defeng Sun, Kim Chuan Toh,  Naihua Xiu, “Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method”,  Journal of Machine Learning Research 20(106):1--25, 2019.

·         Shenglong Hu, Defeng Sun, Kim Chuan Toh, “Best nonnegative rank-one approximations of tensors”, SIAM Journal on Matrix Analysis and Applications 40 (2019) XXX--XXX. 

·         Ying Cui, Defeng Sun, Kim Chuan Toh, “Computing the best approximation over the intersection of a polyhedral set and the doubly nonnegative cone”, SIAM Journal on Optimization 29 (2019) XXX--XXX. 

·         Meixia Lin, Yong-Jin Liu, Defeng Sun, Kim Chuan Toh,  “Efficient sparse Hessian based algorithms for the clustered lasso problem”, SIAM Journal on Optimization 29 (2019) 2026--2052. 

·         Liang Chen, Defeng Sun, Kim Chuan Toh,  Ning Zhang,  A Unified Algorithmic Framework of Symmetric Gauss-Seidel Decomposition based Proximal ADMMs for Convex Composite Programming”, Journal of Computational Mathematics (2019)

·         Liang Chen, Defeng Sun, Kim Chuan Toh,   Some Problems on the Gauss-Seidel Iteration Method in Degenerate Cases”, Journal On Numerical Methods and Computer Applications, 40 (2019) 98--110 (in Chinese)

·         Ying Cui and Defeng Sun, and Kim Chuan Toh,  On the R-superlinear convergence of  the KKT residuals generated by the augmented Lagrangian method for  convex  composite conic programming”,   Mathematical Programming 178 (2019) 1--29  [DOI: 10.1007/s10107-018-1300-6] https://arxiv.org/abs/1706.08800

·         Xudong Li, Defeng Sun, and Kim Chuan Toh,  A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications”, Mathematical Programming 175 (2019) 395--418. arXiv:1703.06629

 

Theses of Students:

2018

·         Yancheng Yuan, Defeng Sun and Kim Chuan Toh,  An efficient semismooth Newton based algorithm for convex clustering”, Proceedings of the 35-th International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR 80, 2018.

·         Xin Yee Lam, J.S. Marron, Defeng Sun, and Kim Chuan Toh,  Fast algorithms for large scale generalized distance weighted discrimination”, Journal of Computational and Graphical Statistics 27 (2018) 368--379.  arXiv:1604.05473.

·         Xudong Li, Defeng Sun, and Kim Chuan Toh,  QSDPNAL: A two-phase augmented Lagrangian method for convex quadratic semidefinite programming”, Mathematical Programming Computation, 10 (2018) 703--743. https://arxiv.org/pdf/1512.08872.pdf

·         Xudong Li, Defeng Sun, and Kim Chuan Toh,  On efficiently solving the subproblems of a level-set method for fused lasso problems”, SIAM Journal on Optimization 28 (2018) 1842--1862. https://arxiv.org/abs/1512.08872

·         Deren Han, Defeng Sun, and Liwei Zhang, “Linear rate convergence of the alternating direction method of multipliers for convex composite programming’’, Mathematics of Operations Research 43 (2018) 622--637. [Revised from the first part of arXiv:1508.02134, August 2015.]

·         Chao Ding, Defeng Sun, Jie Sun, and Kim Chuan Toh, Spectral operators of matrices”, Mathematical Programming 168 (2018) 509--531. [Revised from the first part of https://arxiv.org/abs/1401.2269, January 2014.]

·         Ying Cui and Defeng Sun, “A complete characterization on the robust isolated calmness of the nuclear norm regularized convex optimization problems”,   Journal of Computational Mathematics 36(3) (2018) 441--458.

·         Xudong Li, Defeng Sun, and Kim Chuan Toh, “A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems’’, SIAM Journal on Optimization 28 (2018) 433--458.

 [ This paper brought Xudong Li the Best Paper Prize for Young Researchers in Continuous Optimization announced in the ICCOPT 2019 held in Berlin, August 3-8, 2019. This is the only prize given in the flagship international conference on continuous optimization held every three years].

2017

·         Chao Ding, Defeng Sun, and Liwei Zhang, “Characterization of the robust isolated calmness for a class of conic programming problems”, arXiv:1601.07418. SIAM Journal on Optimization 27 (2017) 67--90.

·         Liang Chen, Defeng Sun, and Kim Chuan Toh,  A note on the convergence of ADMM for linearly constrained convex optimization problems”, arXiv:1507.02051. Computational Optimization and Applications 66 (2017) 327--343.  [In this note a comprehensive proof is supplied to clarify many ambiguities/incorrect proofs in the literature].

·         Liang Chen, Defeng Sun, and Kim Chuan Toh, An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming”, arXiv:1506.00741. Mathematical Programming 161 (2017) 237--270.

 

Theses of Students:

 

2016

 

             Theses of Students:

2015

 

 

           Theses of Students:

2014

 

Theses of Students:

2013

 

Theses of Students:

        2012

 

Theses of Students:  

2011

2010

 

Theses of Students:

2009

 

Theses of Students:

2008

2007

2006

2005

 

Theses of Students:

2004

 

Theses of Students:

2003

2002

2001

2000

1999

1998

1997

1996

1995 

 

 

1994

1993

D.F. Sun, ``Projected extragradient method for finding saddle points of general convex programming'', Qufu Shifan Daxue Xuebao Ziran Kexue Ban 19:4 (1993) 10--17.

Return to: Department of Applied Mathematics, The Hong Kong Polytechnic University


Last Modified: September 15, 2019
Defeng Sun, Department of Applied Mathematics, The Hong Kong Polytechnic University