Papers

D. Oliveira, H. Wolkowicz and Y. Xu. ADMM for the SDP relaxation of the QAP. Accepted in Mathematical Programming Computation, 2018. [code] [arXiv]
Y. Xu. Asynchronous parallel primal-dual block update methods. Accepted in Computational Optimization and Applications, 2018. [arXiv] [Slides]
Y. Ouyang and Y. Xu. Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems. Submitted, 2018. [arXiv]
Y. Xu. Primal-dual stochastic gradient method for convex programs with many functional constraints. Submitted, 2018. [arXiv]
Y. Xu. First-order methods for constrained convex programming based on linearized augmented Lagrangian function. Submitted, 2017. [arXiv]
Y. Xu. Iteration complexity of inexact augmented Lagrangian methods for constrained convex programming. Submitted, 2017. [arXiv]
X. Gao, Y. Xu and S. Zhang. Randomized primal-dual proximal block coordinate updates. Submitted, 2016. [arXiv]
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]

B. Liu, T. Xie, Y. Xu, M. Ghavamzadeh, Y. Chow, D. Lyu and D. Yoon. A Block Coordinate Ascent Algorithm for Mean-Variance Optimization, NIPS, 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.
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, 2018.

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, 2017. [arXiv]
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]

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]
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] [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] [demo] [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. [pdf][code]

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]
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]