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
Supervisor: Professor Jane Juanjuan Ye
随机规划 / 鲁棒优化
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：国家自然科学基金面上项目, "Splitting method for large scale optimization problems and their applications'', 2019 - 2023. 在研
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. 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. 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‘’, 2020-2022. 在研
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
尧伟（2020.3 - Present）, from Wuhan University (B.Sc), Chinese University of Hong Kong (M.Phi, Ph.D)
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)
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
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, P. Mu and J. Zhang, On the convergence of ADMM with task adaption and beyond, 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.
Y.C. Liu and J. Zhang, Confidence Regions of Stochastic Variational Inequalities: Error Bound Approach, 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, X.M. Yuan and J. Zhang, Discrete Approximation Scheme in Distributionally Robust Optimization, preprint 2018.
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, Preprint 2018
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.