| Stochastic first-order methods for convex and nonconvex functional constrained optimization D Boob, Q Deng, G Lan Mathematical Programming 197 (1), 215-279, 2023 | 70 | 2023 |
| Boosting Techniques for Physics‐Based Vortex Detection L Zhang, Q Deng, R Machiraju, A Rangarajan, D Thompson, DK Walters, ... Computer Graphics Forum 33 (1), 282-293, 2014 | 37 | 2014 |
| An uncertainty-driven approach to vortex analysis using oracle consensus and spatial proximity A Biswas, D Thompson, W He, Q Deng, CM Chen, HW Shenk, ... 2015 IEEE pacific visualization symposium (PacificVis), 223-230, 2015 | 28 | 2015 |
| Mitigating inventory overstocking: Optimal order‐up‐to level to achieve a target fill rate over a finite horizon Y Tan, AA Paul, Q Deng, L Wei Production and Operations Management 26 (11), 1971-1988, 2017 | 21 | 2017 |
| Optimal adaptive and accelerated stochastic gradient descent Q Deng, Y Cheng, G Lan arXiv preprint arXiv:1810.00553, 2018 | 17 | 2018 |
| New Developments of ADMM-based Interior Point Methods for Linear Programming and Conic Programming Q Deng, Q Feng, W Gao, D Ge, B Jiang, Y Jiang, J Liu, T Liu, C Xue, Y Ye, ... arXiv preprint arXiv:2209.01793, 2022 | 15 | 2022 |
| Minibatch and momentum model-based methods for stochastic weakly convex optimization Q Deng, W Gao Advances in Neural Information Processing Systems 34, 23115-23127, 2021 | 11 | 2021 |
| Level constrained first order methods for function constrained optimization D Boob, Q Deng, G Lan Mathematical Programming, 1-61, 2024 | 10 | 2024 |
| A feasible level proximal point method for nonconvex sparse constrained optimization D Boob, Q Deng, G Lan, Y Wang Advances in Neural Information Processing Systems 33, 16773-16784, 2020 | 8 | 2020 |
| Efficiency of coordinate descent methods for structured nonconvex optimization Q Deng, C Lan Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020 | 6 | 2020 |
| Trust region methods for nonconvex stochastic optimization beyond Lipschitz smoothness C Xie, C Li, C Zhang, Q Deng, D Ge, Y Ye Proceedings of the AAAI Conference on Artificial Intelligence 38 (14), 16049 …, 2024 | 2 | 2024 |
| Gbm-based bregman proximal algorithms for constrained learning Z Lin, Q Deng arXiv preprint arXiv:2308.10767, 2023 | 1 | 2023 |
| First-order methods for Stochastic Variational Inequality problems with Function Constraints D Boob, Q Deng arXiv preprint arXiv:2304.04778, 2023 | 1 | 2023 |
| Stochastic Dimension-reduced Second-order Methods for Policy Optimization J Liu, C Xie, Q Deng, D Ge, Y Ye arXiv preprint arXiv:2301.12174, 2023 | 1 | 2023 |
| Sketched Newton Value Iteration for Large-Scale Markov Decision Processes J Liu, C Xie, Q Deng, D Ge, Y Ye Proceedings of the AAAI Conference on Artificial Intelligence 38 (12), 13936 …, 2024 | | 2024 |
| Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization Z Lin, J Xia, Q Deng, L Luo Proceedings of the AAAI Conference on Artificial Intelligence 38 (16), 17477 …, 2024 | | 2024 |
| A Low-Rank ADMM Splitting Approach for Semidefinite Programming Q Han, C Li, Z Lin, C Chen, Q Deng, D Ge, H Liu, Y Ye arXiv preprint arXiv:2403.09133, 2024 | | 2024 |
| Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization W Gao, Q Deng Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity W Gao, Q Deng arXiv preprint arXiv:2401.13971, 2024 | | 2024 |
| An accelerated primal‐dual method for semi‐definite programming relaxation of optimal power flow Z Shi, X Wang, D Yan, S Chen, Z Lin, J Xia, Q Deng IET Energy Systems Integration 5 (4), 477-490, 2023 | | 2023 |
