| Primer on monotone operator methods EK Ryu, S Boyd Appl. comput. math 15 (1), 3-43, 2016 | 437 | 2016 |
| Plug-and-play methods provably converge with properly trained denoisers E Ryu, J Liu, S Wang, X Chen, Z Wang, W Yin International Conference on Machine Learning, 5546-5557, 2019 | 434 | 2019 |
| Large-scale convex optimization: algorithms & analyses via monotone operators EK Ryu, W Yin Cambridge University Press, 2022 | 217 | 2022 |
| Decentralized proximal gradient algorithms with linear convergence rates SA Alghunaim, EK Ryu, K Yuan, AH Sayed IEEE Transactions on Automatic Control 66 (6), 2787-2794, 2020 | 138 | 2020 |
| Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices H Monajemi, S Jafarpour, M Gavish, Stat 330/CME 362 Collaboration, ... Proceedings of the National Academy of Sciences 110 (4), 1181-1186, 2013 | 129 | 2013 |
| Accelerated algorithms for smooth convex-concave minimax problems with O (1/k^ 2) rate on squared gradient norm TH Yoon, EK Ryu International Conference on Machine Learning, 12098-12109, 2021 | 128 | 2021 |
| Structural characterization of unsaturated phosphatidylcholines using traveling wave ion mobility spectrometry HI Kim, H Kim, ES Pang, EK Ryu, LW Beegle, JA Loo, WA Goddard, ... Analytical chemistry 81 (20), 8289-8297, 2009 | 111 | 2009 |
| Operator splitting performance estimation: Tight contraction factors and optimal parameter selection EK Ryu, AB Taylor, C Bergeling, P Giselsson SIAM Journal on Optimization 30 (3), 2251-2271, 2020 | 110 | 2020 |
| A parallel method for earth mover’s distance W Li, EK Ryu, S Osher, W Yin, W Gangbo Journal of Scientific Computing 75 (1), 182-197, 2018 | 92 | 2018 |
| Stochastic proximal iteration: a non-asymptotic improvement upon stochastic gradient descent EK Ryu, S Boyd Author website, early draft, 25, 2014 | 71 | 2014 |
| Extensions of Gauss quadrature via linear programming EK Ryu, SP Boyd Foundations of Computational Mathematics 15 (4), 953-971, 2015 | 69 | 2015 |
| Finding the forward-Douglas–Rachford-forward method EK Ryu, BC Vũ Journal of Optimization Theory and Applications 184 (3), 858-876, 2020 | 61 | 2020 |
| Branch-and-bound performance estimation programming: A unified methodology for constructing optimal optimization methods S Das Gupta, BPG Van Parys, EK Ryu Mathematical Programming 204 (1), 567-639, 2024 | 54 | 2024 |
| Exact optimal accelerated complexity for fixed-point iterations J Park, EK Ryu International Conference on Machine Learning, 17420-17457, 2022 | 50 | 2022 |
| Uniqueness of DRS as the 2 operator resolvent-splitting and impossibility of 3 operator resolvent-splitting EK Ryu Mathematical Programming 182 (1), 233-273, 2020 | 50 | 2020 |
| Ode analysis of stochastic gradient methods with optimism and anchoring for minimax problems EK Ryu, K Yuan, W Yin arXiv preprint arXiv:1905.10899, 2019 | 47 | 2019 |
| Diffusion probabilistic models generalize when they fail to memorize TH Yoon, JY Choi, S Kwon, EK Ryu ICML 2023 workshop on structured probabilistic inference {\&} generative …, 2023 | 43 | 2023 |
| A new use of Douglas–Rachford splitting for identifying infeasible, unbounded, and pathological conic programs Y Liu, EK Ryu, W Yin Mathematical Programming 177 (1), 225-253, 2019 | 41* | 2019 |
| Scaled relative graphs: Nonexpansive operators via 2D Euclidean geometry EK Ryu, R Hannah, W Yin Mathematical Programming 194 (1), 569-619, 2022 | 40 | 2022 |
| Vector and matrix optimal mass transport: theory, algorithm, and applications EK Ryu, Y Chen, W Li, S Osher SIAM Journal on Scientific Computing 40 (5), A3675-A3698, 2018 | 40 | 2018 |