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张进

副教授  

0755-88015915 http://faculty.sustech.edu.cn/zhangj9/

  • 简历
  • 科研
  • 教学
  • 发表论著

个人简介


张进,籍贯安徽省安庆市岳西县,南方科技大学数学系/深圳国家应用数学中心 副教授,2007、2010年本科、硕士毕业于大连理工大学,2014年博士毕业于加拿大维多利亚大学。2015至2018年间任职香港浸会大学数学系,2019年初加入南方科技大学。致力于最优化理论和应用研究,代表性成果发表在Math Program、SIAM J Optim、Math Oper Res、SIAM J Numer Anal、J Mach Learn Res、IEEE Trans Pattern Anal Mach Intell,以及ICML、NeurIPS、ICLR等有重要影响力的最优化、计算数学、机器学习期刊与会议上。研究成果获得 中国运筹学会青年科技奖、广东省青年科技创新奖,主持 国家自然科学基金优青、天元重点、面上项目、广东省自然科学基金杰青项目、深圳市科技创新培养人才优青项目、以及科技部重点研发计划“数学与应用数学”专项课题。


Editoral Service: Associate Editor of Numerical Algebra, Control and Optimization (NACO) 


教育背景


2014年,加拿大维多利亚大学,数学与统计系,获 应用数学 哲学博士学位

Ph.D Thesis: Enhanced Optimality Conditions and New Constraint Qualifications for Nonsmooth Optimization Problems

Supervisor: Professor Jane Juanjuan Ye


2010年,大连理工大学,数学科学学院,获 应用数学 理学硕士学位

Supervisor: 林贵华 教授


2007年,大连理工大学,人文社会科学学院,获 新闻学 文学学士学位

工作经历


2022年12月至今,南方科技大学,数学系,Tenure-track 副教授


2019年1月至2022年11月,南方科技大学,数学系,Tenure-track 助理教授


2015年4月至2019年1月,香港浸会大学,数学系,研究助理教授


招收博士   招聘博士后/研究助理教授


长期招聘 研究助理教授/博士后 研究员: Dr. Jin Zhang from Southern University of Science and Technology would like to hire RAP/postdoc. Ideal candidates should be familiar in optimization theory or application. Salary package is competitive and subject to research experience, basic package up to ¥500,000 per year for RAP and 350,000 per year for postdoc. If interested, please send your CV to zhangj9@sustech.edu.cn.


招收2025级博士研究生: I am interested in students (with strong mathematics or computing background, not necessarily majored in optimizationwho are willing to work hard on challenging problems in optimization. Salary package is competitive,  about 110,000 per yearIf interested, please send me an email to request for more details on our PhD programs. 


研究领域、代表性论文

最优化理论:变分分析,非光滑分析,扰动分析(2010 - present, start this topic since entering the Ph.D. program in UViC

M. Benko, H. Gfrerer, J.J. Ye*, J. Zhang and J.C. ZhouSecond-order optimality conditions for general nonconvex optimization problems and variational analysis of disjunctive systems, SIAM Journal on Optimization 2023

J.S. Chen, J.J. Ye*, J. Zhang and J.C. Zhou, Exact formula for the second-order tangent set of the second-order cone complementarity setSIAM Journal on Optimization 29, no. 4 (2019) 2986–3011. 

K. Bai, J.J. Ye* and J. Zhang, Directional quasi/pseudo-normality as sufficient conditions for metric subregularity, SIAM Journal on Optimization 29, no. 4 (2019) 2625—2647.

L. Guo, G.H. Lin, J.J. Ye* and J. Zhang, Sensitivity analysis of the value function for parametric mathematical programs with equilibrium constraints. SIAM Journal on Optimization, 24, no. 3 (2014), 1206--1237. 

L. Guo, J.J. Ye* and J. Zhang, Mathematical programs with geometric constraints in Banach spaces: enhanced optimality, exact penalty, and sensitivity. SIAM Journal on Optimization, 23, no. 4, (2013), 2295--2319. 

J.J. Ye* and J. Zhang, Enhanced Karush-Kuhn-Tucker condition and weaker constraint qualifications. Mathematical Programming, 139, no. 1-2 (2013), 353--381. 


双层规划方法在机器学习中应用(2019 - present, start these two bilevel related topics since arriving at SUSTech

R.S. Liu, Y.H. Liu, S.Z. Zeng and J. Zhang*, Towards Gradient-based Bilevel Optimization with Non-convex Followers and BeyondConference on Neural Information Processing Systems (NeurIPS) Spotlight (< 3% out of 9122 submissions) 2021

R.S. Liu, X. Liu, X.M. Yuan, S.Z. Zeng and J. Zhang*, A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization,  International Conference on Machine Learning (ICML) 2021

R.S. Liu, P. Mu, X.M. Yuan, S.Z. Zeng and J. Zhang*, A generic first-order algorithmic framework for bi-Level programming beyond lower-level singleton, International Conference on Machine Learning (ICML) 2020 (广东省计算数学会青年优秀学术成果奖)

R.S. Liu, P. Mu, X.M. Yuan, S.Z. Zeng and J. Zhang*, A Generic Descent Aggregation Framework for Gradient-based Bi-level Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
J.J. Ye, X.M. Yuan, S.Z. Zeng and J. Zhang*, Difference of convex algorithms for bilevel programs with applications in hyperparameter selection,  Mathematical Programming 2022b

R.S. Liu, L. Ma, X.M. Yuan, S.Z. Zeng and J. Zhang*, Task-Oriented Convex Bilevel Optimization with Latent FeasibilityIEEE Transactions on Image Processing 2022

L Gao, J.J. Ye, H.A. Yin, S.Z. Zeng and J. Zhang*. Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems, International Conference on Machine Learning (ICML) 2022a

R.S. Liu, X. Liu, S.Z. Zeng, J. Zhang* and Y.X. Zhang, Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training, International Conference on Machine Learning (ICML) 2022b

L. Guo, J.J. Ye and J. Zhang*, Sensitivity analysis of the maximal value function with applications in nonconvex minimax programs, Mathematics of Operations Research, 2023

R.S. Liu, X. Liu, W. Yao, S.Z. Zeng and J. Zhang*, Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong ConvexityInternational Conference on Machine Learning (ICML) 2023

R.S. Liu, X. Liu, S.Z. Zeng, J. Zhang* and Y.X. Zhang, Value-Function-based Sequential Minimization for Bi-level Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence 2023a

R.S. Liu, X. Liu, S.Z. Zeng, J. Zhang* and Y.X. Zhang, Hierarchical Optimization-Derived LearningIEEE Transactions on Pattern Analysis and Machine Intelligence 2023b

W. Yao, C.M. Yu, S.Z. Zeng and J. Zhang*, Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm, International Conference on Learning Representations (ICLR) spotlight presentation (<5% out of 7262 submissions), 2024

R.S. Liu, Z. Liu, W. Yao, S.Z. Zeng and J. Zhang*, Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy, International Conference on Machine Learning (ICML)  2024.

T.S. Chu, D.C. Xu, W. Yao and J. Zhang*, SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity, International Conference on Machine Learning (ICML)  2024.


双层规划理论在经济学中应用

R.Z. Ke, W. Yao, J.J. Ye and J. Zhang*, Generic property of the partial calmness condition for bilevel programming problems, SIAM Journal on Optimization, 2022a


基于变分分析的优化算法收敛分析(2015 - present, start this topic since arriving at HKBU

X.M. Yuan, S.Z. Zeng and J. Zhang*, Discerning the linear convergence of ADMM for structured convex optimization through the lens of variational analysis, Journal of Machine Learning Research 21, (2020) 1-75.

Y.C. Liu, X.M. Yuan, S.Z. Zeng and J. Zhang*, Partial error bound conditions and the linear convergence rate of ADMM, SIAM Journal on Numerical Analysis 56, no. 4 (2018) 2095—2123.

J.J. Ye, X.M. Yuan*, S.Z. Zeng and J. Zhang, Variational analysis perspective on linear convergence of some first order methods for nonsmooth convex optimization problems,  Set-Valued and Variational Analysis 2021 (the most satisfied paper)

B. Mordukhovich*, X.M. Yuan, S.Z. Zeng and J. Zhang*, A globally convergent proximal Newton-type method in nonsmooth convex optimizationMathematical Programming 2022a


随机规划 / 鲁棒优化(2007 - present, start this topic since entering the master program in DUT

L Chen, Y.C. Liu, X.M. Yang* and J. Zhang, Stochastic approximation methods for the two-stage stochastic linear complementarity problem, SIAM Journal on Optimization, 2022b

G.H. Lin*, M.J. Luo, D.L. Zhang and J. Zhang, Stochastic second-order-cone complementarity problems: expected residual minimization formulation and its applications, Mathematical Programming, 165, no.1 (2017), 197-233.


基金项目


主持: Research Grants Council of Hong Kong, "Linear convergence of the (randomized block coordinate) proximal gradient methods via variational analysis'', 2018 - 2021. 30万. (离职中止)

主持: 国家自然科学基金 青年项目, "关于规模化双层规划问题的最优性与算法研究'', 2017 - 2019. 20万. (结题)

related research: SIOPT 2019a、SIOPT 2019b、IEEE TIP 2022


主持: 国家自然科学基金 面上项目, "基于变分分析的分裂算法线性收敛率研究'', 2020 - 2023. 52万 (在研)

related research: SVVA 2021、JMLR 2020、IEEE TIP 2021、JOTA 2022、MP 2022a


主持:深圳市高等院校稳定支持计划 面上项目, ‘’双层规划模型研究及其在契约理论中的应用‘’, 2021-2023. 50万 (结题)

related research: SIOPT 2022a、ORT 2021、JCA 2023


主持:深圳市优秀科技创新人才培养 优青项目, ‘’元学习和超参数学习驱动的双层规划模型与算法‘’, 2021-2024. 180万 (在研)

related research: ICML 2021、ICML 2022b、NeurIPS 2021 spotlight、IEEE TPAMI 2022


主持:广东省自然科学基金 杰青项目, ”双层规划理论、方法与应用“, 2022-2025, 100万(在研

related research: ICML 2022a、SIOPT 2022b、MP 2022b


主持:国家自然科学基金 优青项目, ”最优化理论与方法“, 2023-2025, 200万(在研)

related research: MOR 2023、ICML 2023、SIOPT 2023、IEEE TPAMI 2023a、IEEE TPAMI 2023b、SMC 2024


主持:科技部重点研发计划项目课题,“面向多模影像数据的深度学习模型研究”,2024-2028,320万(在研)

related research: ICLR 2024 spotlight、ICML 2024a


主持:国家自然科学基金 数学天元重点项目, "大模型约化的数学理论与方法", 2024-2025(在研)

related research: ICML 2024b


科研奖项

广东省科技厅 青年科技创新奖(2022)

中国运筹学会 青年科技奖 (2020)

南方科技大学 理学院青年科研奖 (2020)

Courses-taught:


MATH3205: Linear and Integer Programming, Fall 2016, Hong Kong Baptist University


MATH3427: Real Analysis, Fall 2016, Hong Kong Baptist University


MATH1006: Advanced Calculus, Spring 2018, Hong Kong Baptist University


MA210:  Operations Research, Spring 2019/2020/2021/2022/2023, Southern University of Science and Technology


MA433:  Optimization Theory and Method, Fall 2019/2020, Southern University of Science and Technology


MA100:  Calculus, Fall 2020, Southern University of Science and Technology


MAT7083: Convex Optimization AlgorithmFall 2021/2022/2023, Southern University of Science and Technology




课题组成员:


研究助理教授:

尧伟 博士(2022.6 - 至今), 武汉大学 (本科), 香港中文大学 (博士),南方科技大学 (博士后研究员)


长期访问学者:

晁棉涛 副教授 (2022.1 - 2023.1) , 广西大学

胡清洁 教授 (2022.1 - 2023.1) , 桂林电子科技大学

白旷 助理教授 (2022.2 - 2022.8), 香港理工大学

朱江醒 副教授 (2021.1 - 2022.1) , 云南大学

李培丽 副教授 (2023.7 至今 ) 河南大学


博士后研究人员:

尧伟 博士(2020.4 - 2022.5), 武汉大学 (本科), 香港中文大学 (博士)

罗璇 博士 ( 2021.8 -  至今), 华中科技大学 (本科), 香港城市大学 (博士)

胡春海 博士(2022.6 至今), 云南大学  (本科/博士)

毛伟豪 博士 (2023.3 至今), 武汉理工大学 (本科), 厦门大学  (博士)

尹海安 博士 (2023.7 - 至今), 东南大学 (本科), 南方科技大学 (硕士/博士)


博士研究生:

尹海安(2019.9 - 2023.6), 东南大学 (本科), 南方科技大学 (硕士). 南科大2023届优秀博士学位论文,中国运筹学会第一届“运筹新人”邀请报告,现 深圳国家应用数学中心 博士后研究员

宋一侠(2021.9 - 至今), 郑州大学 (本科), 南方科技大学 (硕士)

杨佩璇 ( 2022.9 - 至今),吉林大学 (本科), 南开大学 (硕士)

白晓宁 (2023.9 - 至今),东北大学(本科), 北京理工大学(硕士)

张旅刚(2024.9 - ),吉林大学(本科),直博

曹啟超(2024.9 - ), 电子科技大学 (本科/硕士)


联合培养博士研究生:

马耀帅 (主导师:鹏城实验室 王晓 教授, 2022.9 - 至今), 辽宁师范大学 (本科), 广西大学 (硕士)

张艺萱 (主导师:香港理工大学 陈小君 教授, 2022.9 至今), 北京师范大学 (本科), 南方科技大学 (硕士)

李珊珊 (主导师:鹏城实验室 王晓 教授, 2023.9 - ), 电子科技大学 (硕士)


硕士研究生:

宋一侠(2018.9 - 2021.7), 郑州大学 (本科)现 南方科技大学 博士研究生

余承铭(2019.9 2023.6), 大连理工大学 (本科)

张艺萱(2020.9 - 2022.7), 北京师范大学 (本科),南科大理学院2022届优秀毕业生,南科大2022届优秀硕士学位论文,现 香港理工大学 博士研究生

孙凯祺(2021.9 至今), 湘潭大学 (本科)

王非凡 (2022.9 至今), 南方科技大学 (本科)

陈澄(2022.9 至今), 杭州电子科技大学 (本科)

章志豪 (2022.9 - 至今 ), 吉林大学 (本科)


访问博士研究生:

杨振平(2019.1 - 2019.7), 上海大学,现 嘉应学院 副教授

曾尚志(2019.9 - 2020.3, 2020.7- 2021.9), 香港大学,现 维多利亚大学 PIMS 博士后研究员, 合作导师Jane Ye教授

丁彦昀(2020.6 - 2023.6), 北京工业大学,现 深圳职业技术学院 讲师

马笑笑 (2020.8 - 2021.8), 维多利亚大学

褚天舒(2023.3 - 至今), 北京工业大学

代表著作(My co-authored works always list the authors in the alphabetical order of their names to indicate equal contributions, except the works in collaboration with mainland students due to their graduation requirements):


Y.S. Ma, X. Wang, W. Yao and J. Zhang, Beyond Convexity: Federated Bilevel Optimization Algorithms with Convergent Guaranteespreprint 2024.

W. Yao, H.A. Yin, S.Z. Zeng and J. Zhang, Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functionspreprint 2024.

L. Guo, H.A. Yin and J Zhang, A Penalized Sequential Convex Programming Approach for Continuous Network Design Problems, preprint 2024.

R.Z. Ke, C. Ryan, W. Yao and J. Zhang, A max-min reformulation approach to nonconvex bilevel optimizationpreprint 2023.

X.X. Ma, W. Yao, J.J. Ye and J. Zhang, Calm local optimality  for nonconvex-nonconcave minimax problems, preprint 2023.

L Gao, J.J. Ye, H.A. Yin, S.Z. Zeng and J. Zhang, Moreau Envelope Based Difference-of-weakly-Convex Reformulation and Algorithm for Bilevel Programs, preprint 2023.

R.S. Liu, Y.H. Liu, S.Z. Zeng and J. Zhang, Augmenting Iterative Trajectory for Bilevel Optimization: Methodology, Analysis and Extensions, preprint 2023.

D. Wang, S.Z. Zeng and J. Zhang, A modularized algorithmic framework for interface related optimization problems using characteristic functions, preprint 2022.  

M. Gao, W. Ouyang, J. Zhang and J.X. Zhu, Generalized metric subregularity for generalized subsmooth multifunctions in Asplund spacepreprint 2022. 

Y.W. Li, G.H. Lin, J. Zhang and X.D. Zhu, A novel approach for bilevel programs based on Wolfe duality, preprint 2021.




R.S. Liu, Z. Liu, W. Yao, S.Z. Zeng and J. Zhang, Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy, International Conference on Machine Learning (ICML)  2024.

T.S. Chu, D.C. Xu, W. Yao and J. Zhang, SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity, International Conference on Machine Learning (ICML)  2024.

X.M. Yang, W. Yao, H.A. Yin, S.Z. Zeng and J. Zhang, Gradient-based Algorithms for Multi-Objective Bi-Level OptimizationSCIENCE CHINA Mathematics 2024 (special issue on AI Methods for Optimization Problems)

W. Yao, C.M. Yu, S.Z. Zeng and J. Zhang, Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm, ICLR 2024 spotlight presentation (<5% out of 7262 submissions)

X.F. Wang, S.Z. Zeng, J. Zhang and J.C. Zhou, Proximal-based Methods can Guarantee Blunt Local Minimizer for Nonconvex Nonsmooth Optimization ProblemOperations Research Transactions 2023 (in Chinese) 

R.S. Liu, X. Liu, S.Z. Zeng, J. Zhang and Y.X. Zhang, Hierarchical Optimization-Derived LearningIEEE Transactions on Pattern Analysis and Machine Intelligence 2023

R.S. Liu, X. Liu, S.Z. Zeng, J. Zhang and Y.X. Zhang, Value-Function-based Sequential Minimization for Bi-level Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence 2023

K. Bai, Y.X. Song and J. Zhang, Second-order enhanced optimality conditions and constraint qualifications, Journal of Optimization Theory and Applications 2023

M. Benko, H. Gfrerer, J.J. Ye, J. Zhang and J.C. ZhouSecond-order optimality conditions for general nonconvex optimization problems and variational analysis of disjunctive systems, SIAM Journal on Optimization 2023

G.H. Lin, Z.P. Yang, H.A. Yin and J. Zhang, Dual-based stochastic inexact algorithm for a class of stochastic nonsmooth convex composite problems, Computational Optimization and Applications 2023.

R.S. Liu, X. Liu, W. Yao, S.Z. Zeng and J. Zhang, Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong ConvexityInternational Conference on Machine Learning (ICML) 2023.

L. Guo, J.J. Ye and J. Zhang, Sensitivity analysis of the maximal value function with applications in nonconvex minimax programs, Mathematics of Operations Research, 2023.  Available at arXiv:2303.01474

X.X. Ma, W. Yao, J.J. Ye and J. Zhang, Combined approach with second-order optimality conditions for bilevel programming problem, Journal of Convex Analysis 2023 (special issue in honor of Roger J-B Wets on his 85th birthday). Available at arXiv:2108.00179v2

J.J. Ye, X.M. Yuan, S.Z. Zeng and J. Zhang, Difference of convex algorithms for bilevel programs with applications in hyperparameter selection, Mathematical Programming 2022Available at arXiv (2102.09006).

L Chen, Y.C. Liu, X.M. Yang and J. Zhang, Stochastic approximation methods for the two-stage stochastic linear complementarity problem, SIAM Journal on Optimization 2022.

L Gao, J.J. Ye, H.A. Yin, S.Z. Zeng and J. Zhang. Value Function based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems, International Conference on Machine Learning (ICML) 2022. Available at arXiv (2206.05976).

R.S. Liu, X. Liu, S.Z. Zeng, J. Zhang and Y.X. Zhang, Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training, International Conference on Machine Learning (ICML) 2022.  

B. Mordukhovich, X.M. Yuan, S.Z. Zeng and J. Zhang, A globally convergent proximal Newton-type method in nonsmooth convex optimizationMathematical Programming 2022. Available at arXiv:2011.08166

R.S. Liu, P. Mu, X.M. Yuan, S.Z. Zeng and J. Zhang, A generic descent aggregation framework for gradient-based bilevel optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence 2022. (pdf

R.S. Liu, L. Ma, X.M. Yuan, S.Z. Zeng and J. Zhang, Task-Oriented Convex Bilevel Optimization with Latent FeasibilityIEEE Transactions on Image Processing 2022.

J. Zhang and X.D. Zhu, Linear convergence of prox-SVRG method for separable nonsmooth convex optimization problems under bounded metric subregularity, Journal of Optimization Theory and Applications 2022.

R.Z. Ke, W. Yao, J.J. Ye and J. Zhang, Generic property of the partial calmness condition for bilevel programming problems, SIAM Journal on Optimization, 2022 Available at arXiv (2107.14469).

R.S. Liu, Y.H. Liu, S.Z. Zeng and J. Zhang, Towards Gradient-based Bilevel Optimization with Non-convex Followers and BeyondNeurIPS Spotlight paper (< 3% out of 9122 submissions) 2021

L. Wang, H. Yin and J. Zhang, Density-based Distance Preserving Graph for Graph-based Learning, IEEE Transactions on Neural Networks and Learning Systems, 2021

R.S. Liu, P. Mu and J. Zhang, Investigating Customization Strategies and Convergence Behaviors of Task-specific ADMM, IEEE Transactions on Image Processing 2021

R.Z. Ke and J. Zhang, On the First Order Approach for Bilevel Programming: Moral Hazard CaseOperations Research Transactions 2021 (in Chinese) (pdf)

J.J. Ye, X.M. Yuan, S.Z. Zeng and J. Zhang, Variational analysis perspective on linear convergence of some first order methods for nonsmooth convex optimization problems,  Set-Valued and Variational Analysis 2021 (special issue dedicated to Tyrrell Rockafellar's 85th birthday) (pdf)

R.S. Liu, X. Liu, X.M. Yuan, S.Z. Zeng and J. Zhang, A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization,  International Conference on Machine Learning (ICML) 2021 (pdf)

Y.C. Liu and J. Zhang, Confidence Regions of Stochastic Variational Inequalities: Error Bound Approach, Optimization, 2020, (pdf)

Y.C. Liu, X.M. Yuan and J. Zhang, Discrete Approximation Scheme in Distributionally Robust Optimization, Numerical Mathematics: Theory, Methods and Applications, 2020, (pdf)

X.F. Wang, J.J. Ye, X.M. Yuan, S.Z. Zeng and J. Zhang, Perturbation techniques for convergence analysis of proximal gradient method and other first-order algorithms via variational analysis, Set-Valued and Variational Analysis, 2020 (pdf)

R.S. Liu, P. Mu, X.M. Yuan, S.Z. Zeng and J. Zhang, A generic first-order algorithmic framework for bi-Level programming beyond lower-level singleton, International Conference on Machine Learning (ICML) 2020 (pdf, supplementaryslides)

C. Fang, X.Y. Ma, J. Zhang and X.D. Zhu, Personality information sharing in supply chain systems for innovative products in the circular economy era, International Journal of Production Research, 2020. (pdf)

X.M. Yuan, S.Z. Zeng and J. Zhang, Discerning the linear convergence of ADMM for structured convex optimization through the lens of variational analysis, Journal of Machine Learning Research 21, (2020) 1-75. (75 pages long paperpdf)

J.S. Chen, J.J. Ye, J. Zhang and J.C. Zhou, Exact formula for the second-order tangent set of the second-order cone complementarity set, SIAM Journal on Optimization 29, no. 4 (2019) 2986–3011.(pdf)

K. Bai, J.J. Ye and J. Zhang, Directional quasi/pseudo-normality as sufficient conditions for metric subregularity, SIAM Journal on Optimization 29, no. 4 (2019) 2625—2647.(pdf)

Y.C. Liu, X.M. Yuan, S.Z. Zeng and J. Zhang, Partial error bound conditions and the linear convergence rate of ADMM, SIAM Journal on Numerical Analysis 56, no. 4 (2018) 2095—2123. (pdf)

Y.C. Liu, H.F. Xu, S. Yang and J. Zhang, Distributionally robust equilibrium for continuous games: Nash and Stackelberg models, European Journal of Operational Research 265 no. 2 (2018) 631—643.(pdf)
Y.C. Liu, X.M. Yuan, S.Z. Zeng and J. Zhang, Primal-dual hybrid gradient method for distributionally robust optimization problem, Operational Research Letters 45 no. 6, (2017) 625—630.(pdf)
G.H. Lin, M.J. Luo, D.L. Zhang and J. Zhang, Stochastic second-order-cone complementarity problems: expected residual minimization formulation and its applications, Mathematical Programming, 165, no.1 (2017), 197-233. (pdf)
G.H. Lin, M.J. Luo and J. Zhang, Smoothing and SAA method for stochastic programming problems with non-smooth objective and constraints. Journal of Global Optimization 66, no. 3 (2016), 487--510. 
L. Guo, G.H. Lin, J.J. Ye and J. Zhang, Sensitivity analysis of the value function for parametric mathematical programs with equilibrium constraints. SIAM Journal on Optimization, 24, no. 3 (2014), 1206--1237. (pdf)
J.J. Ye and J. Zhang, Enhanced Karush-Kuhn-Tucker conditions for mathematical programs with equilibrium constraints. Journal of Optimization Theory and Applications 163, no. 3 (2014), 777--794. (pdf)
L. Guo, J.J. Ye and J. Zhang, Mathematical programs with geometric constraints in Banach spaces: enhanced optimality, exact penalty, and sensitivity. SIAM Journal on Optimization, 23, no. 4, (2013), 2295--2319. (pdf)
J.J. Ye and J. Zhang, Enhanced Karush-Kuhn-Tucker condition and weaker constraint qualifications. Mathematical Programming, 139, no. 1-2 (2013), 353--381. (pdf)


Publications with mainland students supervised and co-supervised

R.S. Liu, J.X. Gao, J. Zhang, D.Y. Meng and Z.C. Lin, Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond,  IEEE Transactions on Pattern Analysis and Machine Intelligence 2021

Z.P. Yang, J. Zhang, Y.L. Wang and G.H. Lin, Variance-Based Subgradient Extragradient Method for Stochastic Variational Inequality Problems, Journal of Scientific Computing, 2021, 

X.D. Zhu, J. Zhang, J.C. Zhou and X.M. Yang, Mathematical programs with second-order cone complementarity constraints: strong stationarity and approximation method, Journal of Optimization Theory and Applications, 2019

P. Zhang, J. Zhang, G.H. Lin and X.M. Yang, Constraint qualifications and proper Pareto optimality conditions for multiobjective problem with equilibrium constraints, Journal of Optimization Theory and Applications, 176 no. 3 (2018) 763—782

G.X. Wang, J. Zhang, B. Zeng and G.H. Lin, Expected residual minimization formulation for a class of stochastic linear second-order cone complementarity problems, European Journal of Operational Research 265 no. 2 (2018). 437—447

S.H. Jiang, J. Zhang, C.H. Chen and G.H. Lin, Smoothing partial exact penalty splitting method for mathematical programs with equilibrium constraints, Journal of Global Optimization, (2017). DOI: 10.1007/s10898-017-0539-4

Y. Zhao, J. Zhang, X.M. Yang and G.H. Lin, Expected residual minimization formulation for a class of stochastic vector variational inequalities. Journal of Optimization Theory and Applications 175 no. 2 (2017), 545--566.