1. He B. S., Xu S. J. and Yuan X. M., Extensions of ADMM for separable convex optimization problems with linear equality or inequality constraints, manuscript, 2021;
2. Ye J. J., Yuan X. M., Zeng S. Z. and Zhang J., Difference of convex algorithms for bilevel programs with applications in hyperparameter selection, manuscript, 2021;
3. Mordukhovich B., Yuan X. M., Zeng S. Z. and Zhang J., A globally convergent proximal Newton-type method in nonsmooth convex optimization, manuscript, 2020;
4. Glowinski R., Song Y. C., Yuan X. M., and Yue H. R., Bilinear optimal control of an advection-reaction-diffusion system, SIAM Review, to appear;
5. Ye J. J., Yuan X. M., Zeng S. Z. and Zhang J., Variational analysis perspective of linear convergence of some first order methods for nonsmooth convex optimization problems, Set-Valued and Variational Analysis, to appear;
6. Wang X. F., Ye J. J., Yuan X. M., Zeng S. Z. and Zhang J., Linear convergence of the proximal gradient method for nonsmooth nonconvex optimization problems via variational analysis, Set-Valued and Variational Analysis, to appear;
7. Tian W. Y., Yuan X. M. and Yue H. R., An ADMM-Newton-CNN numerical approach to TV models for identifying discontinuous diffusion coefficients in elliptic equations: convex case with gradient observation; Inverse Problems, 37, 085004 (29pp), 2021;
8. Glowinski R., Song Y. C. and Yuan X. M., An ADMM numerical approach to linear parabolic state constrained optimal control problems, Numerische Mathematik, 144, 931-966, 2020;
9. Yuan X. M., Zeng S. Z. and Zhang J., Discerning the linear convergence of ADMM for structured convex optimization through the lens of variational analysis, Journal of Machine Learning Research, 21, 1-75, 2020;
10. He B. S., Ma F. and Yuan X. M., Optimal proximal augmented Lagrangian method and its application to full Jacobian splitting for multi-block separable convex minimization problems, IMA Journal of Numerical Analysis, 75, 361-388, 2020;
11. He B. S., Ma F. and Yuan X. M., Optimally linearizing the alternating direction method of multipliers for convex programming, Computational Optimization and Applications, 75(2), 361-388, 2020;
12. Song Y. C., Yuan X. M. and Yue H. R., An inexact Uzawa algorithmic framework for a class of nonlinear saddle point problems with application to elliptic optimal control problems, SIAM Journal on Numerical Analysis, 57(6), 2656-2684, 2019;
13. Tian W. Y. and Yuan X. M., Faster alternating direction method of multipliers with O(1/n2) convergence rate, Mathematics of Computation, 88, 1685-1713, 2019;
14. Tian W. Y. and Yuan X. M., An accelerated primal-dual iterative scheme for the L2-TV regularized model of linear inverse problems, Inverse Problems, 35, 035002, 2019;
15. Liu Y., Yuan X. M., Zeng S. and Zhang J., Partial error bound conditions for the linear convergence rate of the alternating direction method of multipliers, SIAM Journal on Numerical Analysis, 56(4), 2095-2123, 2018;
16. Yue H., Yang Q., Wang X. F, and and Yuan X. M., Implementing the alternating direction method of multipliers to big datasets: A case study of least absolute shrinkage and selection operator, SIAM Journal of Scientific Computing, 40(5), A3121-A3156, 2018;
17. Guo K., Han D. R. and Yuan X. M., Convergence analysis of Douglas-Rachford splitting method for "strongly + weakly" convex programming, SIAM Journal on Numerical Analysis, 55 (4),1549-1577, 2017;
18. He B. S., Tao M. and Yuan X. M., Convergence rate and iteration complexity on the alternating direction method of multipliers with a substitution procedure for separable convex programming, Mathematics of Operations Research, 42 (3), 662-691, 2017;
19. Tao M. and Yuan X. M., Accelerated Uzawa methods for convex optimization, Mathematics of Computation, 86, 1821-1845, 2017;
20. Dai Y. H., Han D. R., Yuan X. M. and Zhang W. X., A sequential updating scheme of Lagrange multiplier for separable convex programming, Mathematics of Computation, 86, 315-343, 2017;
21. Tian W. Y. and Yuan X. M., Linearized primal-dual methods for linear inverse problems with total variation regularization and finite element discretization; Inverse Problems, 32, 115011 (32pp), 2016;
22. He B. S., Ma F. and Yuan X. M., Convergence analysis of the symmetric version of ADMM, SIAM Journal on Imaging Sciences, 9(3), 1467-1501, 2016;
23. Chen C., He B. S., Ye Y. Y. and Yuan X. M., The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent, Mathematical Programming, 155, 57-79, 2016;
24. He B. S. and Yuan X. M., On convergence rate of the Douglas-Rachford operator splitting method, Mathematical Programming, 153, 715-722, 2015;
25. He B. S. and Yuan X. M., On nonergodic convergence rate of Douglas-Rachford alternating direction method of multipliers, Numerische Mathematik, 130 (3), 567-577, 2015;
26. Li M. and Yuan X. M., A strictly contractive Peaceman-Rachford splitting method with logarithmic-quadratic proximal regularization for convex programming, Mathematics of Operations Research, 40, 842-858, 2015;
27. Li X. X. and Yuan X. M., A proximal strictly contractive Peaceman-Rachford splitting method with applications to imaging, SIAM Journal on Imaging Sciences, 8(2), 1332-1365, 2015;
28. He B. S., Hou L. S. and Yuan X. M., On full Jacobian decomposition of the augmented Lagrangian method for separable convex programming, SIAM Journal on Optimization, 25(4), 2274-2312, 2015;
29. Fang X.Y., He B.S., Liu H. and Yuan X. M., Generalized alternating direction method of multipliers: New theoretical insights and applications, Mathematical Programming Computation, 7(2), 149-187, 2015;
30. He B. S., Tao M. and Yuan X.M., A splitting method for separable convex programming, IMA Journal of Numerical Analysis, 35(1), 394-426, 2015;
31. Li X., Zhao T., Yuan X. M. and Liu H., An R package are for high dimensional linear regression and precision matrix estimation, Journal of Machine Learning Research, 16, 553-557, 2015;
32. Qi H. and Yuan X. M., Computing the nearest Euclidean distance matrix with low embedding dimensions, Mathematical Programming, 147, 351-389, 2014;
33. Corman E. and Yuan X. M., A generalized proximal point algorithm and its convergence rate, SIAM Journal on Optimization, 24 (4), 1614-1638, 2014;
34. Han D. R., Yuan X. M. and Zhang W.X., An augmented-Lagrangian-based parallel splitting method for separate convex programming with applications to image processing, Mathematics of Computations, 83, 2263-2291 2014;
35. He B.S., You Y. F. and Yuan X. M., On the convergence of primal-dual hybrid gradient algorithm, SIAM Journal on Imaging Sciences, 7(4), 2526-2537, 2014;
36. Han D. R., He H.J., Yang H. and Yuan X. M., A customized Douglas-Rachford splitting algorithm for separable convex minimization with linear constraints, Numerische Mathematik, 127, 167-200, 2014;
37. He B. S., Liu H., Wang Z. and Yuan X. M., A strictly contractive Peaceman-Rachford splitting method for convex programming, SIAM Journal on Optimization, 24(3), 1101-1140, 2014;
38. Han D. R. and Yuan X. M., Local linear convergence of alternating direction method of multipliers for quadratic programs, SIAM Journal on Numerical Analysis, 51(6), 3446-3457, 2013;
39. Yang W. H. and Yuan X. M., The globally uniquely solvable property of second-order cone linear complementarity problems, Mathematical Programming, 141, 295-317, 2013;
40. Chan R. H., Tao M., and Yuan X. M., Constrained total variational deblurring models and fast algorithms based on alternating direction method of multipliers, SIAM Journal on Imaging Sciences, 6 (1), 680-697, 2013;
41. Yang J. F. and Yuan X. M., Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization, Mathematics of Computation; 82 (281), 301-329, 2013;
42. Tao M. and Yuan X. M., On the O(1/t) convergence rate of alternating direction method with logarithmic-quadratic proximal regularization, SIAM Journal on Optimization, 22 (4), 1431-1448, 2012;
43. He B. S and Yuan X. M., On the O(1/n) convergence rate of Douglas-Rachford alternating direction method, SIAM Journal on Numerical Analysis, 50, 700-709, 2012;
44. Wang X. F. and Yuan X. M., The linearized alternating direction method for Dantzig selector, SIAM Journal on Scientific Computing, 34 (5), pp. A2792 - A2811, 2012;
45. He B. S., Tao M. and Yuan X. M., Alternating direction method with Gaussian back substitution for separable convex programming, SIAM Journal on Optimization, 22, 313-340, 2012;
46. He B. S. and Yuan X. M., Convergence analysis of primal-dual algorithms for a saddle-point problem: from contraction perspective, SIAM Journal on Imaging Sciences, 5 (1), 119-149, 2012;
47. Chen C. H., He B. S. and Yuan X. M., Matrix completion via alternating direction methods, IMA Journal of Numerical Analysis, 32, 227-245,2012;
48. Yuan X. M., Alternating direction method of multipliers for covariance selection models, Journal of Scientific Computing, 51, 261-273, 2012;
49. Tao M. and Yuan X. M., Recovering low-rank and sparse components of matrices from incomplete and noisy observations, SIAM Journal on Optimization, 21(1), 57-81, 2011;
50. Yuan X. M. and Li M., A LQP-based decomposition method for solving a class of variational inequalities, SIAM Journal on Optimization, 21(4), 1309-1318, 2011;
51. Ng M. K., Wang F. and Yuan X. M., Inexact alternating direction methods and applications in image processing, SIAM Journal on Scientific Computing, 33(4), 1643-1668, 2011;
52. Chan R. H., Yang J. F. and Yuan X. M., Alternating direction method for image inpainting in wavelet domain, SIAM Journal on Imaging Sciences, 4, 807-826, 2011;
53. He B. S., Xu M. H. and Yuan X. M. Solving large-scale least squares covariance matrix problems by alternating direction methods, SIAM Journal of Matrix Analysis and Applications, 32(1), pp. 136-152, 2011;
54. Ng M. K., Weiss P. A and Yuan X.M ., Solving constrained total-variation image reconstruction problems via alternating direction methods, SIAM Journal on Scientific Computing, 32(5),2710-2736, 2010;