| A primer on coordinate descent algorithms HJM Shi, S Tu, Y Xu, W Yin arXiv preprint arXiv:1610.00040, 2016 | 113 | 2016 |
| Methods for quantized compressed sensing HJM Shi, M Case, X Gu, S Tu, D Needell 2016 Information Theory and Applications Workshop (ITA), 1-9, 2016 | 38 | 2016 |
| Optimal occlusion uniformly partitions red blood cells fluxes within a microvascular network SS Chang, S Tu, KI Baek, A Pietersen, YH Liu, VM Savage, SPL Hwang, ... PLoS computational biology 13 (12), e1005892, 2017 | 33 | 2017 |
| A two-stage decomposition approach for AC optimal power flow S Tu, A Wächter, E Wei IEEE Transactions on Power Systems 36 (1), 303-312, 2020 | 20 | 2020 |
| Recent developments in security-constrained AC optimal power flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition I Aravena, DK Molzahn, S Zhang, CG Petra, FE Curtis, S Tu, A Wächter, ... Operations Research 71 (6), 1997-2014, 2023 | 15 | 2023 |
| Practical approximate projection schemes in greedy signal space methods C Garnatz, X Gu, A Kingman, J LaManna, D Needell, S Tu arXiv preprint arXiv:1409.1527, 2014 | 7 | 2014 |
| Optimizing quantization for Lasso recovery X Gu, S Tu, HJM Shi, M Case, D Needell, Y Plan IEEE Signal Processing Letters 25 (1), 45-49, 2017 | 6 | 2017 |
| A decomposition algorithm with fast identification of critical contingencies for large-scale security-constrained AC-OPF FE Curtis, DK Molzahn, S Tu, A Wächter, E Wei, E Wong Operations Research 71 (6), 2031-2044, 2023 | 5* | 2023 |
| Two-Stage Decomposition Algorithms and Their Application to Optimal Power Flow Problems S Tu Northwestern University, 2021 | 3 | 2021 |
| A note on practical approximate projection schemes in signal space methods X Gu, D Needell, S Tu | 3* | 2015 |
| Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization K Huang, Y Wu, X Zhang, S Tu, Q Wu, M Wang, H Wang arXiv preprint arXiv:2206.14846, 2022 | 1 | 2022 |
