Education

• Joint Ph.D. Louisiana State University, Co-advisor: Prof. Hongchao Zhang
• Ph.D. Xi’an Jiaotong University, Advisor: Prof. Jicheng Li
• M.S. Guilin University of Electronic Technology, Advisor: Prof. Xuefeng Duan
• B.S. Yan’an University, Supervisor: Prof. Jinbao Guo

Academic Experience

• March, 2019-Now, Northwestern Polytechnical University, Associate professor
• Dec. 2019-Now, Northwestern Polytechnical University, Postdoctor, co-advisor: Prof. Hao Sun
• April-August, 2021, Chinese Academy of Sciences, Visiting Prof. Yuhong Dai
• Oct. - Oct. 2019, Beihang University, Visiting Prof. Deren Han
• Oct. - Oct. 2016, Southern University of Science and Technology, Visiting Prof. Bingsheng He

Teaching

• Ph.D course: [Frontier Topics in Mathematics]
• M.S. course: [Optimization Theory and Method], [Machine Learning, Dynamical Systems, and Control], [Convex Algorithm and Applications]
• B.S. course: [Linear Algebra]

Research Interests

Currently I am interested in stochastic algorithms (e.g. stochastic gradient method, ADMM and proximal point algorithm, primal-dual method) for solving the non-smooth convex/nonconvex programming problems arising in machine learning, statistical learning, image processing and so forth. Wellcome to my official homepage for more details.

Research Grants

• National Natural Science Foundation of China: Study on Structured Optimization Algorithms in Large-scale Data Processing, 2021-2023, Lead
• China Postdoctoral Science Foundation: Novel Alternating Direction Method with Applications, 2021-2022, Lead
• Fundamental Research Funds for the Central Universities: Study on First-order Algorithms for Structured Convex Optimization, 2019-2021, Lead
• National Statistical Science Research Project of China: Analysis Method for Low Rank Sparse Big-data with Applications, 2018-2020, Participant
• National Natural Science Foundation of China: Study on Model and Algorithm for Multilevel Structured Low Rank Approximation and Sparse Representation, 2017-2020, Participant
• Natural Science Foundation of Fujian province: Study on Estimating the Solution to Linear Complementarity Problem with Its New Algorithms, 2016-2019, Participant

Academic Achievements

★ Optimization methods including first-order methods

• Y. Ma, J. Bai, H. Sun. An inexact ADMM with proximal-indefinite term for sepable convex optimization, (2021) In preparation
• J. Bai, M. Zhang, H. Zhang. An inexact ADMM for sepable nonconvex and nonsmooth optimization, (2021) Under review
• J. Bai, H. Zhang. An new insight on augmented Lagrangian method and its extensions, (2021) Optimization Online
• J. Bai, F. Bian, X. Chang, L. Du. Accelerated stochastic Peaceman-Rachford method for empirical risk minimization, (2021) Under review
• Q. Kong, H. Sun, J. Bai. Optimization of the core for an unbalanced cooperative game, (2021) Under review
• J. Bai, J. Liang, K. Guo, Y. Jing, H. So. Accelerated symmetric ADMM and its applications in signal processing, (2019) Under review
• J. Bai, D. Han, H. Sun, H. Zhang. Convergence on a symmetric accelerated stochastic ADMM with larger stepsizes, CSIAM Transactions on Applied Mathematics, Accepted, (2021)
• J. Bai, W. Hager, H. Zhang. An inexact accelerated stochastic ADMM for separable convex optimization, Computational Optimization and Applications, Accepted, (2021)
• X. Chang,J. Bai(Corresponding author). A projected extrapolated gradient method with larger step size for monotone variational inequalities, Journal of Optimization Theory and Applications, 190: 602-627, (2021) (JCR-2)
• J. Bai, Y. Ma, H. Sun, M. Zhang. Iteration complexity analysis of a partial LQP-based alternating direction method of multipliers, Applied Numerical Mathematics, 165: 500-518, (2021) (JCR-2)
• J. Bai, X. Chang, J. Li, F. Xu. Convergence revisit on generalized symmetric ADMM, Optimization, 70: 149-168, (2021) (JCR-3)
• Y. Bai, Y. Gong, J. Bai, et al. A joint analysis of multi-paradigm fMRI data with its applications to cognitive study, IEEE Transactions on Medical Imaging, 40(3): 951-962, (2021) (JCR-1, Top)
• X. Chang, J. Bai(Corresponding author), D. Song, S. Liu. Linearized symmetric multi-block ADMM with indefinite proximal regularization and optimal proximal parameter, Calcolo, 57: 38 (2020) (JCR-2)
• J. Bai, J. Li, Z. Wu. Several variants of the primal-dual hybrid gradient algorithm with applications, Numerial Mathematics: Theory, Methods and Applications, 13(1): 176-199 (2020) (JCR-2)
• J. Bai, K.Guo, X. Chang. A family of multi-parameterized proximal point algorithms, IEEE Access, 7(1): 164021-164028 (2019) (JCR-2, Top)
• J. Bai, J. Li, P. Dai, J. Li. General parameterized proximal point algorithm with applications in the statistical learning, International Journal of Computer Mathematics, 96(1): 199-215 (2019) (JCR-3)
• J. Bai, J. Li, F. Xu. Accelerated method for optimization over density matrices in quantum state estimation, Linear and Multilinear Algebra, 66(5):869-880 (2018) (JCR-3)
• J. Bai, H. Zhang, J. Li. A parameterized proximal point algorithm for separable convex optimization, Optimization Letters, 12(7):1589-1608 (2018) (JCR-3)
• J. Bai, J. Li, F. Xu, H. Zhang. Generalized symmetric ADMM for separable convex optimization, Computational Optimization and Applications, 70(1):129-170 (2018) (JCR-2)

★ Numerical algebra including low-rank approximation & complementarity problem

• J. Bai, C. Chen, X. Gu. Two alternating iterative methods and their preconditioned versions for solving the absolute value equations, (2021) In preparation
• P. Dai, J. Bai, J. Li. A general preconditioner for tensor complementarity problem, (2020) Under review
• P. Dai, J. Li, J. Bai, L. Dong. Notes on new error bounds for linear complementarity problems of Nekrasov matrices, B-Nekrasov matrices and QN-matrices, Numerical Mathematics: Theory, Methods and Applications, 12: 1191-1212 (2019) (JCR-2)
• P. Dai, J. Li, J. Bai, L. Dong. New error bounds for linear complementarity problems of S-Nekrasov matrices and B-S-Nekrasov matrices, Computational and Applied Mathematics, 38: 61(2019) (JCR-3)
• P. Dai, J. Li, J. Bai, J. Qiu. A preconditioned two-step modulus-based matrix splitting iteration method for linear complementarity problem, Applied Mathematics and Computation,348: 542-551 (2019) (JCR-1, Top)
• J. Bai, J. Li, F. Xu, P. Dai. A novel method for a class of structured low rank minimization with equality constraint, Journal of Computational and Applied Mathematics, 330:475-487 (2018) (JCR-2, Top)
• J. Bai, J. Li, J. Deng. A class of multilevel structured low-rank approximation arising in material processing, International Journal of Computer Mathematics, 95(2): 329-340 (2018) (JCR-3)
• Z. Liu, J. Li, G. Li, J. Bai, X. Liu. A new model for sparse and low rank matrix decomposition, Journal of Applied Analysis and Computation, 7(2):600-616 (2017) (JCR-4)
• J. Bai, J. Li, P. Dai. Novel alternating update method for low rank approximation of structured matrices, Applied Numerical Mathematics, 121:223-233 (2017) (JCR-2)
• P. Dai, J. Li, Y. Li, J. Bai. A general preconditioner for linear complementarity problem with an M-matrix, Journal of Computational and Applied Mathematics, 317:100-112 (2017) (JCR-2, Top)
• X. Duan, J. Bai, J. Li, J. Peng. On the low rank solution of the Q-weighted nearest correlation matrix problem, Numerical Linear Algebra with Applications, 23(2):340-355 (2016) (JCR-2)
• J. Bai X. Duan, K. Cheng, X. Zhang. A class of weighted low rank approximation of the positive semidefinite Hankel matrix. Journal of Applied Mathematics. Atricle ID 937 573 (2015) 7 pages.
• X. Duan, J. Bai(Corresponding author), M. Zhang, X. Zhang. On the generalized low rank approximation of the correlation matrices arising in the asset portfolio, Linear Algebra and its Applications, 461:1-17 (2014) (JCR-3)

Professional Activities

★ Editor (2021–): Annals of Applied Sciences

★ Council Member (2021–): CSIAM Activity Group on Big Data and AI

★ Member of International Program Committee, The International Conference on Artificial intelligence & Advanced Networking, 28-29 July 2022

Oxford, United Kingdom

★ Guest commentator of Math Review; Members of ORSC(S390021704M), CSIAM(2600615M), CMS(S010007511M), MIIT Key Laboratory of Dynamics and Control of Complex Systems, etc.

★ Reviewer of the journals

• Automatica (JCR-2)
• Applied Numerical Mathematics (JCR-2)
• Computational and Applied Mathematics (JCR-3)
• Computational Optimization and Applications (JCR-2)
• Calcolo (JCR-2)
• FILOMAT (JCR-4)
• IEEE Access (JCR-2)
• Journal of Mathematics (JCR-4)
• Journal of Global Optimization (JCR-2)
• Journal of the Operations Research Society of China (EI)
• Neurocomputing (JCR-2)
• Numerical Algorithms (JCR-2)
• Optimization Letters (JCR-3)
• Signal Processing (JCR-2)
• Mathematica Numerica Sinica, Chinese Series (CSCD)
• Journal of Numerical Methods and Computer Applications, Chinese Series (CSCD)
• Numerical Mathematics: A Journal of Chinese Universities, Chinese Series (CSCD)
• Operations Research Transactions, Chinese Series (CSCD)

Talks and conference

1. Talks
   • August 15-19, 2021, Report on “Accelerated stochastic ADMM with applications in supervised learning”. The 13-th Annual Conference on Computational Mathematics of Chinese Mathematical Society, Nanjing Normal University, China.
   • June 18, 2021, Report on “Deterministic and stochastic ADMM for structured convex optimization”. Henan Normal University, China. (invited by Prof. Yonggang Pei)
   • April 21, 2021, Report on “Deterministic and stochastic ADMM for structured convex optimization”. Beihang University, China. (invited by Prof. Jiaxin Xie)
   • December 19, 2020, Report on “Deterministic and stochastic ADMM for nonsmooth separable convex optimization”. Changsha University of Science & Technology, China. (invited by Prof. Weijun Zhou)
   • November 5, 2020, Report on “ADMM and its varaints for separable convex optimization”. Guangxi University, China. (invited by Prof. Gonglin Yuan)
   • October 15-18, 2020, Report on “Deterministic and stochastic ADMM for separable convex optimization”. ORSC2020, University of Science and Technology of China, China.
   • November 29, 2019, Report on “ADMM for separable convex and nonconvex optimization”. Xinyang Normal University, China. (invited by Dr. Yongchao Yu)
   • November 4, 2019, Report on “ADMM for separable convex and nonconvex optimization”. Southwestern University of Finance and Economics, China. (invited by Prof. X.M. Gu and J.T. Ma)
   • October 18-19, 2019, Report on “ADMM for separable structured optimization: from deterministic to stochastic, and convex to nonconvex”. 2019 Seminar on Advances in Big-data Optimization，China West Normal University，China.
   • July 31- August 4, 2019, Report on “Generalized symmetric ADMM for separable convex optimization”. The 12-th Conference on Computational Mathematics (Section Chair), Harbin Institute of Technology, China.
   • September 4, 2018, Report on “Deterministic and Stochastic ADMM for Structured Convex Optimization”. Louisiana State University, USA. (invited by Prof. S.C. Brenner and H.C. Zhang)
   • February 21, 2018, Report on “Generalized/linearized symmetric ADMM for separable convex optimization”. Tulane University, USA. (invited by Prof. Y.P. Wang)
   • February 2-3, 2018, Report on “Generalized symmetric ADMM for separable convex optimization”. SCALA 2018 - Scientic Computing aroud Louisiana, Louisiana State University, USA.
   • April 23-27, 2016, Report “On the low rank solution of the Q-weighted nearest correlation matrix problem”. Shenzhen University, China. (invited by Prof. G. Li)
2. Organized conference
   • September 3-5, 2021, Youth Symposium on Computing and Optimization, Supported by Northwestern Polytechnical University, Organizer
   • May 29-30, 2021, “International Conference on Nonconvex and Distributed Optimization: Theory, Algorithm and Applications”, Supported by Tianyuan Mathematical Center in Northwest China, Organizer
   • May 20, 2021, Workshop on Operator Splitting Methods and Its Apllications(II), Speakers: Weijun Zhou and Yuntong Bai, Organizer.
   • March 27, 2021, Workshop on Operator Splitting Methods and Its Apllications(I), Speakers: Jianlin Jiang, Min Li, and Xingju Cai, Organizer.
   • December 27, 2020, Seminar on Optimization Progress(III), Speakers: Caihua Chen, Xiaokai Chang, and Zhongming Wu, Organizer.
   • November 27, 2020, Report on An inexact augmented Lagrange method for solving a family of nonsmooth optimization problem on Riemannian manifold by Prof. Zheng Peng, Address: Math 214.
   • November 21-22, 2020,“International Conference on Computational Geophysics and Inverse Problems for PDE”, Supported by Tianyuan Mathematical Center in Northwest China, Co-organizer
   • November 14, 2020, Seminar on Optimization Progress(II), Speakers: Hezhi Luo, Yaohua Hu, and Cong Sun, Organizer.
   • September 26, 2020, Seminar on Optimization Progress(I), Speakers: Jiawei Chen, Yangyang Xu, and Deren Han, Organizer.

Current Students

• Yang Chen, Master candidate, 2022-2025
• Yuxue Ma, Master candidate (Cooperated with Prof. Hao Sun), 2020-2023

Contact Information

• Address: School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710129, China
• Email: bjc1987@163.com or jianchaobai@nwpu.edu.cn