Zaiwen Wen
Home
Research
Research
Publications
Recent Talks
Softwares
Seminars
Group
Optbook
Databook
Mathematical
Formalization
Workstation
Guidance
Teaching
Optimization 
Methods 
(2024 Fall)
Algorithms for 
Big Data 
Analysis 
(2024 Spring)

Publications

Books

Review papers

  • X. Liu, Z. Wen, Y. Yuan, Subspace Methods for Nonlinear Optimization, CSIAM Transactions on Applied Mathematics (paper)

  • 潘少华, 文再文, 低秩稀疏矩阵优化问题的模型与算法, 运筹学学报 (paper)
    S. Pan, Z. Wen, Models and Algorithms for Low-rank and Sparse Matrix Optimization Problems (in Chinese), Operations Research Transactions (paper)

  • J. Hu, X. Liu, Z. Wen, Y. Yuan, A Brief Introduction to Manifold Optimization, Journal of Operations Research Society of China, (paper)

  • 王奇超，文再文，蓝光辉，袁亚湘, 优化算法复杂度分析, 中国科学：数学 (paper)
    Q. Wang, Z. Wen, G. Lan, Y. Yuan, Complexity Analysis For Optimization Methods (in Chinese), SCIENTIA SINICA Mathematica, (paper)

Selected papers

  • Z. Xie, W. Yin, Z. Wen, ODE-based Learning to Optimize, (paper), (code: “O2O”)

  • Y. Chen, L. Cao, K. Yuan, Z. Wen, Sharper Convergence Guarantees for Federated Learning with Partial Model Personalization, (paper)

  • R. Wang, C. Zhang, S. Pu, J. Gao, Z. Wen, A Customized Augmented Lagrangian Method for Block-Structured Integer Programming, IEEE Transactions on Pattern Analysis and Machine Intelligence, (paper)

  • J. Wu, J. Hu, H. Zhang, Z. Wen, Convergence analysis of an adaptively regularized natural gradient method, IEEE Transactions on Signal Processing, 2024

  • Z. Ke, J. Zhang, Z. Wen, An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks, ICML 2024

  • Z. Deng, K. Deng, J. Hu, Z. Wen, An Augmented Lagrangian Primal-Dual Semismooth Newton Method for Multi-Block Composite Optimization (paper)

  • C. Chen, R. Chen, T. Li, R. Ao, Z. Wen, A Monte Carlo Policy Gradient Method with Local Search for Binary Optimization, (paper) (code: “MCPG”)

  • T. Li, F. Chen, H. Chen, Z. Wen, Provable Convergence of Variational Monte Carlo Methods (paper)

  • Z. Ke, J. Zhang, Z. Wen, Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation (paper)

  • J. Hu, T. Tian, S. Pan, Z. Wen, On the local convergence of the semismooth Newton method for composite optimization, (paper)

  • L. Cao, Z. Wen, Y. Yuan, Some Sharp Error Bounds for Multivariate Linear Interpolation and Extrapolation, (paper)

  • Z. Zhu, F. Chen, J. Zhang, Z. Wen, A Unified Primal-Dual Algorithm Framework for Inequality Constrained Problems, Journal of Scientific Computing, 2023, (paper)

  • J. Hu, R. Ao, A. So, M. Yang, Z. Wen, Riemannian Natural Gradient Methods, SIAM Journal on Scientific Computing, (paper)

  • Y. Liu, H. Liu, H. Nie, Z. Wen, A DRS-based Path-following Algorithm for Linear Programming

  • F. Chen, J. Zhang, Z. Wen, A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP, NeurIPS 2022, (paper)

  • Y. Wang, K. Deng, H. Liu, Z. Wen, A Decomposition Augmented Lagrangian Method for Low-rank Semidefinite Programming, SIAM Journal on Optimization, (paper)

  • H. Liu, K. Deng, H. Liu, Z. Wen, An Entropy-Regularized ADMM For Binary Quadratic Programming, Journal of Global Optimization

  • J. Zhang, Z. Jin, B. Jiang, Z. Wen, Stochastic Augmented Projected Gradient Methods for the Large-Scale Precoding Matrix Indicator Selection Problem, IEEE Transactions on Wireless Communications

  • M. Yang, D. Xu, Q. Cui, Z. Wen, P. Xu, NG+ : A Multi-Step Matrix-Product Natural Gradient Method for Deep Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, (paper) (code)

  • Y. Liu, Z. Wen, W. Yin, A multiscale semi-smooth Newton method for Optimal Transport, Journal of Scientific Computing

  • Y. Li, M. Zhao, W. Chen, Z. Wen, A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning, SIAM Journal on Optimization, (paper)

  • Z. Jin, J. Schmitt, Z. Wen, On the Analysis of Model-free Methods for the Linear Quadratic Regulator, Journal of Operations Research Society of China, (paper)

  • M. Yang, D. Xu, Y. Li, Z. Wen, M. Chen, Sketchy Empirical Natural Gradient Methods for Deep Learning, Journal of Scientific Computing (paper)

  • M. Yang, D. Xu, H. Chen, Z. Wen, M. Chen, Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods, CVPR 2021 (paper)

  • Z. Chen, A. Milzarek, Z. Wen, A Trust-Region Method For Nonsmooth Nonconvex Optimization, Journal of Computational Mathematics (paper)

  • M. Zhao, Y. Li, Z.Wen, A Stochastic Trust-Region Framework for Policy Optimization, Journal of Computational Mathematics (paper)

  • M. Yang, A. Milzarek, Z. Wen, T. Zhang, A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization, Mathematical Programming (paper)

  • H. Zhang, A. Milzarek, Z. Wen, W. Yin, On the geometric analysis of a quartic-quadratic optimization problem under a spherical constraint, Mathematical Programming (paper)

  • L. Wu, X. Liu, Z. Wen, Symmetric rank-1 approximation of symmetric high-order tensors, Optimization Methods and Software

  • B. Jiang, X. Meng, Z. Wen, X. Chen, An exact penalty approach for optimization with nonnegative orthogonality constraints, Mathematical Programming (paper)

  • T. Tian, Y. Cai, X. Wu, Z. Wen, Ground states and their characterization of spin-F Bose-Einstein condensates, SIAM Journal on Scientific Computing (paper)

  • Z. Jia, Z. Wen, Y. Ye, Toward Solving 2-TBSG Efficiently, Optimization Methods and Software, (paper)

  • Y. Wang, Z. Jia, Z. Wen, The Search direction Correction makes first-order methods faster, SIAM Journal on Scientific Computing (paper)

  • Y. Li, H. Liu, Z. Wen, Y, Yuan, Low-rank Matrix Optimization Using Polynomial-filtered Subspace Extraction, SIAM Journal on Scientific Computing (paper), (code)

  • Y. Duan, M. Wang, Z. Wen, Y. Yuan, Adaptive Low-Nonnegative-Rank Approximation for State Aggregation of Markov Chains, SIAM Journal on Matrix Analysis and Applications, (paper)

  • J. Hu, B. Jiang, L. Lin, Z. Wen, Y. Yuan, Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints, SIAM Journal on Scientific Computing (paper)

  • A. Milzarek, X. Xiao, S. Cen, Z. Wen, M. Ulbrich, A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization, SIAM Journal on Optimization, (paper)

  • J. Hu, Z. Wen, A. Milzarek, Y. Yuan, Adaptive Regularized Newton Method for Riemannian Optimization, SIAM Journal on Matrix Analysis and Applications (paper)

  • Y. Li, Z. Wen, C. Yang, Y. Yuan, A Semi-smooth Newton Method For semidefinite programs and its applications in electronic structure calculations, SIAM Journal on Scientific Computing (paper)

  • J. Zhang, H. Liu, Z. Wen, S. Zhang, A Sparse Completely Positive Relaxation of the Modularity Maximization for Community Detection, SIAM Journal on Scientific Computing (paper)

  • H. Yuan, X. Gu, R. Lai, Z. Wen, Global optimization with orthogonality constraints via stochastic diffusion, Journal of Scientific Computing (paper)

  • T. Pang, Q. Li, Z. Wen, Z. Shen, Phase retrieval, a data-driven wavelet frame based approach, Applied and Computational Harmonic Analysis (paper)

  • C. Ma, X. Liu, Z. Wen, Globally Convergent Levenberg-Marquardt Method For Phase Retrieval, IEEE Transactions on Information Theory (paper)

  • C. Chen, Z. Wen, Y. Yuan, A general two-level subspace method for nonlinear optimization, Journal of Computational Mathematics

  • X. Xiao, Y. Li, Z. Wen, L. Zhang, Semi-Smooth Second-order Type Methods for Composite Convex Programs, Journal of Scientific Computing (paper)

  • Y. Yang, B. Dong, Z. Wen, Randomized Algorithms For High Quality Treatment Planning in Volumetric Modulated Arc Therapy, Inverse Problems, (paper)

  • J. Zhang, Z. Wen, Y. Zhang, Subspace Methods With Local Refinements for Eigenvalue Computation Using Low-Rank Tensor-Train Format, Journal of Scientific Computing, (pdf)

  • J. Hu, B. Jiang, X. Liu, Z. Wen, A Note on Semidefinite Programming Relaxations For Polynomial Optimization Over a Single Sphere, Science in China, Mathematics, (pdf)

  • B. Jiang, Y. Liu, Z. Wen, L_p-norm regularization algorithms for optimization over permutation matrices, SIAM Journal on Optimization, (pdf)

  • Z. Wen, Y. Zhang, Block algorithms with augmented Rayleigh-Ritz projections for large-scale eigenpair computation, SIAM Journal on Matrix Analysis and Applications (pdf)

  • X. Wu, Z. Wen, W. Bao, A regularized Newton method for computing ground states of Bose-Einstein condensates, Journal of Scientific Computing, (pdf)

  • P. Qian, Z. Wang, Z. Wen, A Composite Risk Measure Framework for Decision Making under Uncertainty, Journal of Operations Research Society of China (pdf)

  • M, Ulbrich, Z. Wen, C. Yang, D, Klockner, Z. Lu, A proximal gradient method for ensemble density functional theory, SIAM Journal on Scientific Computing, (pdf)

  • Q. Dong, X. Liu, Z. Wen, Y. Yuan, A Parallel Line Search Subspace Correction Method for Composite Convex Optimization, Journal of Operations Research Society of China (pdf)

  • X. Liu, Z. Wen, Y. Zhang, An Efficient Gauss-Newton Algorithm for Symmetric Low-Rank Product Matrix Approximations, SIAM Journal on Optimization, (pdf)

  • X. Liu, Z. Wen, X. Wang, M. Ulbrich, Y. Yuan, On the Analysis of the Discretized Kohn-Sham Density Functional Theory, SIAM Journal on Numerical Analysis, (pdf)

  • X. Zhang, J. Zhu, Z. Wen, A. Zhou, Gradient type optimization methods for electronic structure calculations, SIAM Journal on Scientific Computing (pdf)

  • Z. Wen, C. Yang, X. Liu, and Y. Zhang, Trace-Penalty Minimization for Large-scale Eigenspace Computation, Journal of Scientific Computing, (pdf)

  • X. Liu, X. Wang, Z. Wen, Y. Yuan, On the Convergence of the Self-Consistent Field Iteration in Kohn-Sham Density Functional Theory, SIAM Journal on Matrix Analysis and Applications (pdf)

  • L. Wang, A. Singer, Z. Wen, Orientation Determination from Cryo-EM images Using Least Unsquared Deviation, SIAM Journal on Imaging Sciences (pdf)

  • Z. Wen, X. Peng, X. Liu, X. Bai and X. Sun, Asset Allocation under the Basel Accord Risk Measures (pdf)

  • Z. Wen, A. Milzarek, and M. Ulbrich, and H. Zhang, Adaptive regularized self-consistent field iteration with exact Hessian for electronic structure calculation, SIAM Journal on Scientific Computing (pdf)

  • X. Liu, Z. Wen, Y. Zhang, Limited memory block Krylov subspace optimization for computing dominant singular value decompositions, SIAM Journal on Scientific Computing (pdf) (code: “LMSVD”)

  • Z. Wen, C. Yang, X. Liu and S. Marchesini, Alternating direction methods for classical and ptychographic phase retrieval, Inverse Problems (pdf)

  • Q. Ling, Y. Xu, W. Yin, and Z. Wen, Decentralized low-rank matrix completion, ICASSP 2012 (pdf) (blog)

  • R. Lai, Z. Wen, W. Yin, X. Gu and L. Lui, Folding-free global conformal mapping for genus-0 surfaces by Harmonic energy minimization, Journal of Scientific Computing (pdf)

  • Q. Ling, W. Yin and Z. Wen, Decentralized jointly sparse signal recovery by reweighted lq minimization, IEEE Transactions on Signal Processing (pdf) (blog)

  • Y. Xu, W. Yin, Z. Wen and Y. Zhang, An alternating direction algorithm for matrix completion with nonnegative factors, Frontiers of Mathematics in China, Vol. 7, No. 2, pp. 365-384 (pdf)

  • S. Yuan, Z. Wen and Y. Zhang, Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization, Optimization Methods and Software (pdf)

  • Z. Wen and W. Yin, A feasible method for optimization with orthogonality constraints, Mathematical Programming (pdf) (code)

  • J. Laska, Z. Wen, W. Yin and R. Baraniuk, Fast and accurate signal recovery from 1-bit compressive measurements, IEEE Transactions on Signal Processing (pdf)

  • Z. Wen, W. Yin and Y. Zhang, Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm, Mathematical Programming Computation (pdf) (code: “LMaFit”)

  • Z. Wen, D. Goldfarb and W. Yin, Alternating direction augmented Lagrangian methods for semidefinite programming, Mathematical Programming Computation (pdf) (code: “SDPAD”, beta1, Sept. 2009) (code: “SDPAD”, beta2, Dec. 2012) (data)

  • Z. Wen, D. Goldfarb and K. Scheinberg, Block coordinate descent methods for semidefinite programming, Handbook on Semidefinite, Cone and Polynomial Optimization (pdf)
    Previous version: Row by row methods for semidefinite programming (with D. Goldfarb, S. Ma and K. Scheinberg)

  • Z. Wen, W. Yin, D. Goldfarb and Y. Zhang, A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation, SIAM Journal on Scientific Computing, Vol 32, No. 4, pp. 1832-1857 (pdf) (code: “FPC_AS”)

  • Z. Wen and W. Yin, H. Zhang and D. Goldfarb, On the convergence of an active set method for l_1 minimization, Optimization Methods and Software (pdf)

  • D. Goldfarb, S. Ma, Z. Wen, Solving low-rank matrix completion problems efficiently, Allerton’09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing

  • Z. Wen and D. Goldfarb, Line search multigrid methods for large-scale nonconvex optimization, SIAM Journal on Optimization, Vol. 20, No. 3, pp. 1478-1503 (pdf) (code)

  • D. Goldfarb, Z. Wen and W. Yin, A curvilinear search method for the p-Harmonic flow on sphere, SIAM Journal on Imaging Sciences, Vol. 2, No. 1, pp. 84-109 (pdf) (code)

  • Y. Wang, Z. Wen, Z. Nashed and Q. Sun, Direct fast method for time-limited signal reconstruction, Applied Optics, Vol. 45, No. 13, pp. 3111-3126 (pdf)

  • Z. Wen, Y. Wang, A new trust region algorithm for image restoration, Science in China Series A, Vol. 48, No. 2

  • Z. Wang, Y. Yuan, Z. Wen, A subspace trust region method for large-scale unconstrained optimization, in Y.Yuan, editor, Numerical Linear Algebra and Optimization (Science Press)

Thesis

  • Z. Wen, First-order methods for semidefinite-programming, Ph.D Thesis, Columbia University, Advisor: Prof. Donald Goldfarb, 2009 (pdf)

  • Z. Wen, Least squares and their applications, M.S. Thesis, Institute of Computational Mathematics, Chinese Academy of Sciences, Advisor: Prof. Ya-xiang Yuan, 2004 (pdf (in Chinese))