Yangjing ZHANG 张羊晶

I am currently an assistant professor in Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences.

Before September 2021, I was a research fellow in the Department of Mathematics at National University of Singapore, working with Professor Kim-Chuan Toh.

I obtained my Ph.D degree in the Department of Mathematics from National University of Singapore in May 2019, under the supervisions of Professor Kim-Chuan Toh and Professor Defeng Sun.

Before that, I graduated from Tsinghua University in 2014 with a B.S. in mathematics, where I was advised by Professor Wenxun Xing.

My current research is focused on large scale sparse optimization problems, the design of efficient algorithms for statistical models and graphical models.

[CV] Updated on 25 December 2024

Email: yangjing.zhang [at] amss [dot] ac [dot] cn

zhangyangjing [at] u [dot] nus [dot] edu

GitHub: https://github.com/YangjingZhang

Publications and preprints

Shengxiang Deng, Xudong Li, Yangjing Zhang, Alternating minimization for square root principal component pursuit. arXiv:2501.00471.
Meixia Lin, Yangjing Zhang, Low rank convex clustering for matrix-valued observations. arXiv:2412.17328.
Yuexin Zhou, Chao Ding, Yangjing Zhang, Strong variational sufficiency of nonsmooth optimization problems on Riemannian manifolds. arXiv:2308.06793.
Meixia Lin, Ziyang Zeng, Yangjing Zhang, Multiple regression for matrix and vector predictors: Model, theory, algorithms, and beyond, Electronic Journal of Statistics, 18 (2024), pp. 5563-5600. arXiv:2410.19264.
Meixia Lin, Yangjing Zhang, DNNLasso: Scalable graph learning for matrix-variate data, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 238 (2024), pp 316-324. arXiv:2403.02608. [code]
Yangjing Zhang, Ying Cui, Bodhisattva Sen, Kim-Chuan Toh, On efficient and scalable computation of the nonparametric maximum likelihood estimator in mixture models, Journal of Machine Learning Research, 25 (2024), pp. 1-46. arXiv:2208.07514. [code]
Yangjing Zhang, Kim-Chuan Toh, Defeng Sun, Learning graph Laplacian with MCP, Optimization Methods and Software, 39 (2024), pp. 569-600. arXiv:2010.11559.
Shiwei Wang, Chao Ding, Yangjing Zhang, Xinyuan Zhao, Strong variational sufficiency for nonlinear semidefinite programming and its implications, SIAM Journal on Optimization, 33 (2023), pp. 2988-3011. arXiv:2210.04448.
Hong T.M. Chu, Kim-Chuan Toh, Yangjing Zhang, On regularized square-root regression problems: distributionally robust interpretation and fast computations, Journal of Machine Learning Research, 23 (2022), pp. 1-39. arXiv:2109.03632.
Ning Zhang, Yangjing Zhang, Defeng Sun, Kim-Chuan Toh, An efficient linearly convergent regularized proximal point algorithm for fused multiple graphical Lasso problems, SIAM Journal on Mathematics of Data Science, 3 (2021), pp. 524-543. arXiv:1902.06952. [code]
Yangjing Zhang, Ning Zhang, Defeng Sun, Kim-Chuan Toh, A proximal point dual Newton algorithm for solving group graphical Lasso problems, SIAM Journal on Optimization, 30 (2020), pp. 2197–2220. arXiv:1906.04647.
Yangjing Zhang, Ning Zhang, Defeng Sun, Kim-Chuan Toh, An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems, Mathematical Programming, 179 (2020), pp. 223–263. arxiv:1712.05910. [code]