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张进
副教授
zhangj9@sustech.edu.cn

简历

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,副教授

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

 

科研

研究领域

最优化理论:变分分析,非光滑分析,扰动分析

双层规划 (均衡约束数学规划)理论、算法及在机器学习、理论经济学中应用

误差界条件及一阶优化算法线性收敛率分析

随机规划 / 鲁棒优化

 

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. 离职中止

参与:National Natural Science Foundation of China, "Splitting method for large scale optimization problems and their applications'', 2019 - 2023. 在研

主持: National Natural Science Foundation of China 青年, "关于规模化双层规划问题的最优性与算法研究'', 2017 - 2020. 在研

主持: National Natural Science Foundation of China 面上, "基于变分分析的分裂算法线性收敛率研究'', 2020 - 2023. 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)

 

教学

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

 

Research Group

Postdoctorate Fellows:

尧伟(2020.3 - Present), from Wuhan University (B.Sc), Chinese University of Hong Kong (M.Phi, Ph.D)

Ph.D Students:

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

冯雁飞(2020.8 - Present), from Naikai University (B.Sc, M.Phi)

Master Students:

宋一侠(2018.9 - Present), from Zhengzhou University  (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), from Beijing University of Technology

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

 

发表论著

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):

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 Optimization 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, preprint 2018.

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

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

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 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.
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

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:10.1080/02331934.2019.1591406
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/S0217595919400013

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.