Research

Current interests:

    • Matrix Optimization Problems, in particular, large scale convex quadratic semidefinite programming
    • Efficient algorithms for large scale optimization problems in data science
    • Optimization and decision making under uncertainty

Papers and preprints:

    1. An asymptotically superlinearly convergent semismooth Newton augmented Lagrangian method for Linear Programming (with Defeng Sun and Kim-Chuan Toh), arXiv:1903.09546, 2019
    2. On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming (with Liang Chen, Defeng Sun, and Kim-Chuan Toh), arXiv:1803.10803, 2018
    3. Estimation of Markov chain via rank-constrained likelihood (with Mengdi Wang and Anru Zhang), Proceedings of the 35-th International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR 80:3039-3048, 2018, Supplementary PDF
    4. On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope (with Defeng Sun and Kim-Chuan Toh), arXiv:1702.05934, Mathematical Programming, in print, 2018
    5. On efficiently solving the subproblems of a level-set method for fused lasso problems (with Defeng Sun and Kim-Chuan Toh), SIAM Journal on Optimization, 28 (2018), pp. 1842–1866
    6. A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications (with Defeng Sun and Kim-Chuan Toh), Mathematical Programming, in print, arXiv:1703.06629, 2017, Springer Nature SharedIT
    7. A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems (with Defeng Sun and Kim-Chuan Toh), SIAM Journal on Optimization, 28 (2018), pp. 433–458
    8. QSDPNAL: A two-phase augmented Lagrangian method for convex quadratic semidefinite programming (with Defeng Sun and Kim-Chuan Toh), Mathematical Programming Computation,  10 (2018) 703–743, arXiv:1512.08872, Springer Nature SharedIT
    9. On the convergence of a majorized ADMM for the linearly constrained convex optimization problems of coupled objective functions (with Ying Cui, Defeng Sun, and Kim-Chuan Toh), Journal of Optimization Theory and Applications, 169 (2016), pp. 1013–1041
    10. A Schur complement based proximal ADMM for convex quadratic conic programming and extensions (with Defeng Sun and Kim-Chuan Toh), Mathematical Programming, 155 (2016), pp. 333–373