Recent Publications

9, R. M. Gower, N. Loizou, X. Qian, A. Sailanbayev, E. Shulgin, P. Richtarik, “SGD: general analysis and improved rates”, arXiv:1901.09401. https://arxiv.org/abs/1901.09401

8, X. Qian, Z, Qu, P. Richtarik, “SAGA with arbitrary sampling”, arXiv:1901.08669. https://arxiv.org/abs/1901.08669

7, X. Qian, L.-Z. Liao, J. Sun, and H. Zhu, “The convergent generalized central paths for linearly constrained convex programming”, SIAM J. Optim., 28(2), 1183-1204, 2018. https://epubs.siam.org/doi/pdf/10.1137/16M1104172

6, X. Qian, L.-Z. Liao, and J. Sun, “A strategy of global convergence for the affine scaling algorithm for convex semidefinite programming”, Math. Program., 1-19, 2018. https://link.springer.com/article/10.1007/s10107-018-1314-0

5, B. Li, X. Qian, J. Sun, K. L. Teo, and C. J. Yu, “A model of distributionally robust two-stage stochastic convex programming with linear recourse”, Applied Mathematical Modelling, 58, 86-97, 2018. https://www.sciencedirect.com/science/article/pii/S0307904X17307382

4, X. Qian and L.-Z. Liao, “Generalized affine scaling trajectory analysis for linearly constrained convex programming”, International Symposium on Neural Networks ,Springer, Cham, 139-147, 2018. https://link.springer.com/chapter/10.1007/978-3-319-92537-0_17

3, H. W. Yue, L.-Z. Liao, and X. Qian, “Two interior point continuous trajectory models for convex quadratic programming with bound constraints”, Pac. J. Optim., (to appear)

2, X. Qian and L.-Z. Liao, “Analysis of the primal affine scaling continuous trajectory for convex programming”, Pac. J. Optim., 14(2), 261-272, 2018. http://www.ybook.co.jp/online2/oppjo/vol14/p261.html

1, X. Qian, L.-Z. Liao, and J. Sun, “Analysis of some interior point continuous trajectories for convex programming”, Optimization, 66 (4), 589-608, 2017. https://doi.org/10.1080/02331934.2017.1279160

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