Leaves Optimization Solver Platform  http://leaves.shufe.edu.cn

1.              D. Boob, Q. Deng, G. Lan, Y. Wang, A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization, NeurIPS 2020

2.              Q. Deng and L. Lan, Efficiency of Coordinate Descent Methods For Structured Nonconvex Optimization, ECML 2020

3.              Q. Deng, J. Gao, D. Ge, S. He, B. Jiang, X. Li, Z. Wang, C. Yang, Y. Ye, A Survey on Modern Optimization Theory and Applications, Science China Mathematics, 2020 (in Chinese)

4.              D. Boob, Q. Deng and G. Lan, Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization,  in submission.

5.              Q. Deng, Yi Cheng, Guanghui Lan, Optimal Adaptive and Accelerated Stochastic Gradient Descent,in submission.

6.              Q. Deng, G. Lan and A. Rangara jan. Randomized block subgradient methods for convex nonsmooth and stochastic optimization, arXiv preprint. in submission.

7.              Y. Tan, A. Paul, Q. Deng, L. Wei. Mitigating Inventory Overstocking: Optimal Order‐up‐to Level to Achieve a Target Fill Rate over a Finite Horizon. In Production and Operations Management 2017

8.              Q. Deng, J. Ho and A. Rangara jan. Stochastic Coordinate Descent for Nonsmooth Convex Optimization. In Optimization for Machine Learning Workshop, NIPS 2013.

9.              Biswas, D. Thompson, W. He, Q. Deng, C.M. Chen, H.W. Shen, R. Machiraju, and A. Rangarajan.  An uncertainty-driven approach to vortex analysis using oracle consensus and spatial proximity. PacicVis 2015

10.          L. Zhang, Q. Deng. R. Machiraju, A. Rangarajan, D. Thompson, D.K. Walters and H.W. Shen. Boosting Techniques for Physics-Based Vortex Detection, Computer Graphics Forum, 33(1):282-293, 2014