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张进

助理教授  

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

  • 简历
  • 科研
  • 教学
  • 发表论著

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

2019年1月至今,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



招聘 博士/博士后/研究助理教授


Research assistant professor/Post-doc: 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. If interested, please send your CV to zhangj9@sustech.edu.cn.


PhD Students: I am interested in students who are willing to work hard on challenging problems in optimization. If 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

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. 

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. 


双层规划算法在机器学习中应用

R.S. Liu, Y.H. Liu, S.Z. Zeng and J. Zhang*, Towards Gradient-based Bilevel Optimization with Non-convex Followers and BeyondNeurIPS 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, preprint 2021.

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


双层规划理论在理论经济学中应用

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


误差界条件及优化算法收敛率分析

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 

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


随机规划 / 鲁棒优化

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: 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、MP 2022


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 (在研)

related research: SIOPT 2022a


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、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


科研奖项


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, Southern University of Science and Technology


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


MA210:  Operations Research, Spring 2020, Southern University of Science and Technology


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


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


MA210:  Operations Research, Spring 2021, Southern University of Science and Technology


MAT7083: Convex Optimization, Fall 2021, Southern University of Science and Technology


MA210:  Operations Research, Spring 2022, Southern University of Science and Technology



Research Group:


Research Assistant Professor:

尧伟 博士(2022.6 - Present), from Wuhan University (B.Sc), Chinese University of Hong Kong (M.Phi, Ph.D)


Long-term Visitors:

晁棉涛 副教授 (2022.1 - Present) , from Guangxi University

胡清洁 副教授 (2022.1 - Present) , from Guilin University of Electronic Technology

白旷 助理教授 (2022.2 - Present), from The Hong Kong Polytechnic University

朱江醒 副教授 (2021.1 - 2022.1) , from Yunnan University


Postdoctorate Fellows:

尧伟 博士(2020.4 - 2022.5), from Wuhan University (B.Sc), Chinese University of Hong Kong (M.Phi, Ph.D)

罗璇 博士( 2021.8 -  Present), from Huazhong University of Science and Technology (B.E), City University of Hong Kong (Ph.D)

胡春海 博士(2022.6  Present), from Yunnan University  (B.Sc, Ph.D)


Ph.D Students:

尹海安(2019.9 - Present), from Southeast University (B.Sc), Southern University of Science and Technology  (M.Phi)

宋一侠(2021.9 - Present), from Zhengzhou University  (B.Sc), Southern University of Science and Technology  (M.Phi)


Master Students:

宋一侠(2018.9 - 2021.7), from Zhengzhou University  (B.Sc)

余承铭(2019.9 -Present), from Dalian University of Technology (B.E) 

张艺萱(2020.9 -Present), from Beijing Normal University (B.Sc)

孙凯祺(2021.9 -Present), from Xiangtan University (B.Sc)

王非凡 (2022.9 - ), from Southern University of Science and Technology (B.Sc)


Visiting Ph.D Students:

杨振平(2019.1 - 2019.7), from Shanghai Univeristy

曾尚志(2019.9 - 2020.3, 2020.7- 2021.9), from the University of Hong Kong. (Currently PIMS post-doc, University of Victoria, supervised by Prof Jane Ye)

丁彦昀(2020.6 - Present), from Beijing University of Technology 

马笑笑 (2020.8 - 2021.8), from University of Victoria

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):


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

R.S. Liu, X. Liu, S.Z. Zeng, W. Yao and J. Zhang,  Towards Extremely Fast Bilevel Optimization with Self-governed Convergence Guarantees, preprint 2022.

X.F. Wang, S.Z. Zeng, J. Zhang and J.C. ZhouProximal-based Methods can Guarantee Blunt Local Minimizer for Nonconvex Nonsmooth Optimization Problempreprint 2022.

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, preprint 2022.

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

R.S. Liu, X. Liu, S.Z. Zeng, J. Zhang and Y.X. Zhang, Value-Function-based Sequential Minimization for Bi-level Optimization, preprint 2021, extension of ICML2021.

L. Guo, J.J. Ye and J. Zhang, Sensitivity analysis of the maximal value function with applications in nonconvex minimax programs, preprint 2021.

X.X. Ma, W. Yao, J.J. Ye and J. Zhang, Combined approach with second-order optimality conditions for bilevel programming problem, preprint 2021.

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, preprint 2021.

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

J.J. Ye, X.M. Yuan, S.Z. Zeng and J. Zhang, Difference of convex algorithms for bilevel programs with applications in hyperparameter selection, preprint 2021. (pdf)




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

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. (pdf)

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 (pdf)

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, 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, (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, supplementary,slides)

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.