南方科技大学数学系导师教师师资介绍简介-张进

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张进
助理教授

zhangj9@sustech.edu.cn 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
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7月至2019年1月，香港浸会大学，数学系，研究助理教授
2015年4月至2015年6月，香港浸会大学，数学系，访问学者

Recruitment Notice

Post-doc: Dr. Jin Zhang from Southern University of Science and Technology would like to hire a 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 mean email to request for more detailson 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 Optimization29, 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 Optimization29, 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.

双层规划理论、算法及在机器学习、理论经济学中应用
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
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, preprint 2021, extension of ICML2020.
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.Z. Ke, W. Yao, J.J. Ye and J. Zhang, Generic property of the partial calmness condition for bilevel programming problems, preprint 2020.

误差界条件及一阶优化算法线性收敛率分析
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 Research21,(2020)1-75.
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,to appear
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,to appear
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,preprint 2020.
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)

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)

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)

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‘’, 2021-2023. 180万 (在研)

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

Research Group:

Long-term Visitor:
Prof. 朱江醒 (2021.1 - Present) , from Yunnan University
Postdoctorate Fellows:
Dr. 尧伟（2020.3 - Present）,from WuhanUniversity (B.Sc),ChineseUniversity of Hong Kong (M.Phi, Ph.D)
Ph.D Students:
尹海安（2019.9 -Present）, fromSoutheast University(B.Sc), Southern University of Science and Technology(M.Phi)
冯雁飞（2020.8 -Present）, fromNaikai University(B.Sc, M.Phi)
Master Students:
宋一侠（2018.9 -Present）,from ZhengzhouUniversity(B.Sc)
张艺萱（2020.9 -Present）, from Beijing Normal University (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-Present）, from the University of Hong Kong
丁彦昀（2020.6 - Present）, fromBeijing University of Technology
马笑笑（2020.8 -Present）, 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）：

R.S. Liu, X. Liu, X.M. Yuan, S.Z. Zeng and J. Zhang,Bilevel Meta Optimization: A Unified Framework for Optimization-Derived Learning,preprint 2021.
R.S. Liu, Y.H. Liu, S.Z. Zeng and J. Zhang,Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond,preprint 2021.

J. Zhang and X.D. Zhu,Linear Convergence of Stochastic First-Order Algorithms under Bounded Metric Subregularity, preprint 2021.

R.Z. Ke and J. Zhang, On the First Order Approach for Bilevel Programming: Moral Hazard Case,preprint 2021. (pdf)

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)

R.S. Liu,P. Mu, X.M. Yuan, S.Z. Zeng and J. Zhang,A generic descent aggregation framework for gradient-based bilevel optimization, preprint 2021, extension of ICML2020. (pdf)
L. Wang, H. Yin, J. Zhang,Density-based Distance Preserving Graph for Graph-based Learning,preprint 2021.
B. Mordukhovich,X.M. Yuan, S.Z. Zeng and J. Zhang,A globally convergent proximal Newton-type method in nonsmooth convex optimization,preprint 2020. (pdf)
R.Z. Ke, W. Yao, J.J. Ye and J. Zhang, Generic property of the partial calmness condition for bilevel programming problems,preprint 2020.
L Chen, Y.C. Liu, X.M. Yang and J. Zhang, Stochastic approximation methods for the two-stage stochastic linear complementarity problem,preprint 2020.
R.S. Liu, P. Mu and J. Zhang,Investigating Customization Strategies and Convergence Behaviors of Task-specific ADMM,preprint 2019.
R.S. Liu, L. Ma, X.M. Yuan, S.Z. Zeng and J. Zhang, Task-Oriented Convex BilevelOptimization with Latent Feasibility,preprint 2019.
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),to appear
R.S. Liu, X. Liu, X.M. Yuan, S.Z. Zeng and J. Zhang,A Hessian-free Interior-point Method for Non-convex Bilevel Optimization,International Conference on Machine Learning (ICML) 2021, to appear

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

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

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 Optimization29, 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,preprint 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, 2020, to appear
P. Zhang, J. Zhang, G.H. Lin and X.M. Yang,Some kind of Pareto stationarity for multiobjective problems with equilibrium constraints,Optimization, (2019) doi:10.1080/**.2019.**
P. Zhang, J. Zhang, G.H. Lin and X.M. Yang,New Constraint Qualifications for S-Stationarity for MPEC with Nonsmooth Objective,Asia Pacific Journal of Operational Research, (2019),DOI: 10.1142/S02**013

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). doi.org/10.1007/s10957-018-01464-w
Z.P. Yang, J. Zhang, X.D. Zhu and G.H. Lin, SAA-based infeasible interior-point algorithms for a class of stochastic complementarity problems and their applications, Journal of Computational and Applied Mathematics, 352, (2019) 382—400
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.