Google scholar citations


  • H. Dahal, W. Liu, Y. Xu. Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints. Submitted, 2023. [arXiv]

  • W. Liu, Q. Lin, Y Xu. First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods. Submitted, 2023. [arXiv]

  • G. Mancino-Ball and Y. Xu. Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems. Submitted, 2023. [arXiv]


  • Y. Xu. Decentralized gradient descent maximization method for composite nonconvex strongly-concave minimax problems. SIAM Journal on Optimization, 34(1), 1006–1044, 2024. [arXiv] [Sharable ePrint by SIAM]

  • X. Zhang, G. Mancino-Ball, NS Aybat, Y. Xu. Jointly Improving the Sample and Communication Complexities in Decentralized Stochastic Minimax Optimization. AAAI, 2024. [arXiv]

  • Z. Li, P. Chen, S. Liu, S. Lu, Y. Xu. Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization. Computational Optimization and Applications, 87, 117–147, 2024. [arXiv] [published version]


  • Y. Yan, J. Chen, P. Chen, X. Cui, S. Lu, Y. Xu. Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data. ICML, 202:39035–39061, 2023. [arXiv]

  • G. Mancino-Ball, Y. Xu, J. Chen. A Decentralized Primal-Dual Framework for Non-convex Smooth Consensus Optimization. IEEE Transactions on Signal Processing, 71, 525–538, 2023. [arXiv]

  • N. Zhou, H. Qin, KS Choi, Y. Du, J. Liu, P. Li, X. Huang, K. Chi, Y. Xu. Correntropy based model predictive controller with multi-constraints for robust path trajectory tracking of self-driving vehicle. Journal of the Franklin Institute, 360(10), 6929–6952, 2023.

  • J. Zhang, H. Chao, A. Dhurandhar, P.Y. Chen, A. Tajer, Y. Xu, P. Yan. Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 728–738, 2023. [arXiv]

  • G. Mancino-Ball, S. Miao, Y. Xu, J. Chen. Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization. AAAI, 37(7), 9055–9063, 2023. [arXiv]

  • J. Zhang, H. Chao, A. Dhurandhar, P.Y. Chen, A. Tajer, Y. Xu, P. Yan. When Neural Networks Fail to Generalize? A Model Sensitivity Perspective. AAAI, 37(9), 11219–11227, 2023. [arXiv]

  • Q. Lin and Y. Xu. Reducing the complexity of two classes of optimization problems by inexact accelerated proximal gradient method. SIAM Journal on Optimization, 33(1), 1–35, 2023. [pdf] [arXiv longer version]

  • Yangyang Xu and Yibo Xu. Momentum-based variance-reduced proximal stochastic gradient method for composite nonconvex stochastic optimization. Journal of Optimization Theory and Applications, 196, 266–297, 2023. [arXiv] [published version]

  • Yangyang Xu, Yibo Xu, Y. Yan, C. Sutcher-Shepard, L. Grinberg and J. Chen. Parallel and distributed asynchronous adaptive stochastic gradient methods. Mathematical Programming Computation, 15, 471–508, 2023. [arXiv] [published version]

  • N. Zhou, K. Choi, B. Chen, Y. Du, J. Liu, Y. Xu. Correntropy-Based Low-Rank Matrix Factorization With Constraint Graph Learning for Image Clustering. IEEE Transactions on Neural Networks and Learning Systems. 34(12), 10433–10446, 2023.


  • Y. Xu. First-order methods for problems with O(1) functional constraints can have almost the same convergence rate as for unconstrained problems. SIAM Journal on Optimization, 32(3), 1759–1790, 2022. [pdf] [arXiv]

  • Q. Lin, R. Ma and Y. Xu. Complexity of an inexact proximal-point penalty method for constrained smooth non-convex optimization, Computational Optimization and Applications, 82, 175–224, 2022. [published version] [arXiv]

  • Yangyang Xu, Yibo Xu, Y. Yan, and J. Chen. Distributed stochastic inertial-accelerated methods with delayed derivatives for nonconvex problems. SIAM Journal on Imaging Sciences, 15(2):550–590, 2022. [pdf]

  • Y. Yan and Y. Xu. Adaptive Primal-Dual Stochastic Gradient Method for Expectation-constrained Convex Stochastic Programs. Mathematical Programming Computation, 14, 319–363, 2022. [published version] [arXiv]

  • Z. Li, P. Chen, S. Liu, S. Lu, Y. Xu. Zeroth-order Optimization for Composite Problems with Functional Constraints. AAAI, 36(7), 7453–7461, 2022. [arXiv]


  • Yibo Xu and Yangyang Xu. Katyusha Acceleration for Convex Finite-Sum Compositional Optimization. INFORMS Journal on Optimization, 3(4):418–443, 2021. [arXiv]

  • Z. Li, P. Chen, S. Liu, S. Lu, Y. Xu. Rate-improved Inexact Augmented Lagrangian Method for Constrained Nonconvex Optimization. AISTATS, 130, 2170–2178, 2021. [arXiv]

  • Z. Li and Y. Xu. Augmented Lagrangian based first-order methods for convex-constrained programs with weakly-convex objective. INFORMS Journal on Optimization, 3(4):373-397, 2021. [arXiv]

  • Y. Xu. Iteration complexity of inexact augmented Lagrangian methods for constrained convex programming. Mathematical Programming, Series A, 185, 199–244, 2021. [published version] [arXiv]

  • Y. Ouyang and Y. Xu. Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems. Mathematical Programming, Series A, 185, 1–35, 2021. [published version] [arXiv]

  • Y. Xu. First-order methods for constrained convex programming based on linearized augmented Lagrangian function. INFORMS Journal on Optimization, 3(1), 89–117, 2021. [published version]


  • Y. Xu. Primal-dual stochastic gradient method for convex programs with many functional constraints. SIAM Journal on Optimization, 30(2), 1664–1692, 2020. [arXiv] [Slides]

  • N. Zhou, B. Chen, T. Jiang, Y. Du and Y. Xu. Maximum Correntropy Criterion based Robust Semi-supervised Concept Factorization for Image Representation. IEEE Transactions on Neural Networks and Learning Systems, 31(10), 3877–3891, 2020.

  • C. Wu and Y. Xu. Greedy coordinate descent method on non-negative quadratic programming. 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 1–5. [arXiv]

  • T. Sun, Y. Sun, Y. Xu and W. Yin. Markov Chain Block coordinate descent. Computational Optimization and Applications, 75(1), 35–61, 2020. [published version] [arXiv]


  • X. Gao, Y. Xu and S. Zhang. Randomized primal-dual proximal block coordinate updates. Journal of the Operations Research Society of China, 7(2), pp. 205–250, 2019. [arXiv]

  • Y. Xu. Asynchronous parallel primal-dual block coordinate update methods for affinely constrained convex programs. Computational Optimization and Applications, 72(1), pp. 87–113, 2019. [arXiv] [Slides]

  • Z. Peng, Y. Xu, M. Yan and W. Yin. On the Convergence of Asynchronous Parallel Iteration with Unbounded Delays. Special issue on Journal of the Operations Research Society of China, 7(1), pp. 5–42, 2019. [pdf]

  • N. Zhou, Y. Xu, H. Chen, Z. Yuan and B. Chen. Maximum Correntropy Criterion based Sparse Subspace Learning for Unsupervised Feature Selection. IEEE Transactions on Circuits and Systems for Video Technology, 29(2), pp. 404–417, 2019.


  • D. Oliveira, H. Wolkowicz and Y. Xu. ADMM for the SDP relaxation of the QAP. Mathematical Programming Computation, 10(4), pp. 631–658, 2018. [code and more] [arXiv]

  • B. Liu, T. Xie, Y. Xu, M. Ghavamzadeh, Y. Chow, D. Lyu and D. Yoon. A Block Coordinate Ascent Algorithm for Mean-Variance Optimization, NeurIPS, pp. 1073–1083, 2018.

  • X. Li, J. Ren, S. Rambhatla, Y. Xu and J. Haupt. Robust PCA via dictionary based outlier pursuit, ICASSP, pp. 4699–4703, 2018.

  • Y. Xu. Hybrid Jacobian and Gauss-Seidel proximal block coordinate update methods for linearly constrained convex programming. SIAM Journal on Optimization, 28(1), pp. 646–670, 2018. [pdf]

  • Y. Chen, J. Zhang and Y. Xu. Adaptive lasso for accelerated hazards models. Journal of Statistical Computation and Simulation, 88(15), pp. 2948–2960, 2018.

  • Y. Xu and S. Zhang. Accelerated Primal-Dual Proximal Block Coordinate Updating Methods for Constrained Convex Optimization. Computational Optimization and Applications, 70(1), 91–128, 2018. [arXiv]

  • Y. Xu. On the convergence of higher-order orthogonality iteration. Linear and Multilinear Algebra, 66(11), pp. 2247–2265, 2018. [arXiv] [Slides]

  • F. Wen and Y. Xu. HOSVD Based Multidimensional Parameter Estimation for Massive MIMO System from Incomplete Channel Measurements. Multidimensional Systems and Signal Processing, 29(4), pp. 1255–1267, 2018.


  • Y. Xu. Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming. SIAM Journal on Optimization, 27(3), 1459–1484, 2017. [pdf]

  • Y. Xu and W. Yin. A globally convergent algorithm for nonconvex optimization based on block coordinate update. Journal of Scientific Computing, 72(2), 700–734, 2017. [arXiv]

  • Y. Xu. Fast algorithms for higher-order singular value decomposition from incomplete data. Journal of Computational Mathematics, Special Issues on Optimization and Structured Solution, 35(4), 395–420, 2017. [arXiv] [code]

2016 and earlier

  • Z. Peng, Y. Xu, M. Yan and W. Yin. ARock: an algorithmic framework for asynchronous parallel coordinate updates. SIAM Journal on Scientific Computing, 38(5), A2851–A2879, 2016. [arXiv] [code and more]

  • Z. Peng, T. Wu, Y. Xu, M. Yan and W. Yin. Coordinate Friendly Structures, Algorithms and applications. Annals of Mathematical Sciences and Applications, 1(1), pp. 57–119, 2016. [arXiv]

  • Y. Xu and W. Yin. A fast patch-dictionary method for whole image recovery. Inverse Problems and Imaging, 10(2), 563–583, 2016. [code and more] [arXiv]

  • N. Zhou, Y. Xu, H. Cheng, J. Fang and W. Pedrycz. Global and local structure preserving sparse subspace learning: an iterative approach to unsupervised feature selection. Pattern Recognition, 53, pp. 87–101, 2016. [arXiv]

  • Y. Xu and W. Yin. Block stochastic gradient iteration for convex and nonconvex optimization. SIAM Journal on Optimization, 25(3), 1686–1716, 2015. [pdf] [Slides]

  • Y. Xu, R. Hao, W. Yin and Z. Su. Parallel matrix factorization for low-rank tensor completion. Inverse Problems and Imaging, 9(2), 601–624, 2015. [pdf] [code]

  • Y. Xu. Alternating proximal gradient method for sparse nonnegative Tucker decomposition. Mathematical Programming Computation, 7(1), 39–70, 2015. [code]

  • Y. Xu, I. Akrotirianakis and A. Chakraborty. Proximal gradient method for huberized support vector machine, Pattern Analysis and Applications, 19(4), 989–1005, 2015. [pdf]

  • Y. Xu, I. Akrotirianakis and A. Chakraborty. Alternating direction method of multipliers for regularized multiclass support vector machines. International Workshop on Machine Learning, Optimization and Big Data, 105–117, 2015. [arXiv]

  • Y. Xu, W. Yin and S. Osher. Learning circulant sensing kernels. Inverse Problems and Imaging 8(3), 901–923, 2014. [paper][code and more]

  • Y. Xu and W. Yin. A block coordinate descent method for regularized multi-convex optimization with applications to nonnegative tensor factorization and completion. SIAM Journal on imaging sciences, 6(3), pp. 1758–1789, 2013. [code and more]

  • M. Lai, Y. Xu and W. Yin. Improved iteratively reweighted least squares for unconstrained smoothed Lq minimization. SIAM Journal on Numerical Analysis, 51(2), pp. 927–957, 2013. [code]

  • Q. Ling, Y. Xu, W. Yin and Z. Wen. Distributed low-rank matrix completion. (ICASSP), pp. 2925–2928, 2012.

  • Y. Xu and J. Cui. Multitask n-Vehicle Exploration Problem: complexity and algorithms. Journal of Systems Science and Complexity, pp. 1080–1092, 2012.

  • Y. Xu, W. Yin, Z. Wen and Y. Zhang. An alternating direction algorithm for matrix completion with nonnegative factors. Journal of Frontiers of Mathematics in China, Special Issues on Computational Mathematics (Springer), pp. 365–384, 2011. [code] [arXiv]

Technical Reports

  • H. Shi, S. Tu, Y. Xu and W. Yin. A Primer on Coordinate Descent Algorithms, 2016. [arXiv]

  • J. Shi, Y. Xu and R. Baraniuk. Sparse bilinear logistic regression, 2014. [arXiv]