| Error bounds, quadratic growth, and linear convergence of proximal methods D Drusvyatskiy, AS Lewis Mathematics of Operations Research 43 (3), 919-948, 2018 | 173 | 2018 |
| Stochastic model-based minimization of weakly convex functions D Davis, D Drusvyatskiy SIAM Journal on Optimization 29 (1), 207–239, 2018 | 157* | 2018 |
| Efficiency of minimizing compositions of convex functions and smooth maps D Drusvyatskiy, C Paquette Mathematical Programming 178 (1), 503-558, 2019 | 100 | 2019 |
| Transversality and alternating projections for nonconvex sets D Drusvyatskiy, AD Ioffe, AS Lewis Foundations of Computational Mathematics 15 (6), 1637-1651, 2015 | 97* | 2015 |
| Stochastic subgradient method converges on tame functions D Davis, D Drusvyatskiy, S Kakade, JD Lee Foundations of computational mathematics 20 (1), 119-154, 2020 | 92 | 2020 |
| Tilt stability, uniform quadratic growth, and strong metric regularity of the subdifferential D Drusvyatskiy, AS Lewis SIAM Journal on Optimization 23 (1), 256-267, 2013 | 77 | 2013 |
| Catalyst for gradient-based nonconvex optimization C Paquette, H Lin, D Drusvyatskiy, J Mairal, Z Harchaoui International Conference on Artificial Intelligence and Statistics, 613-622, 2018 | 72* | 2018 |
| Second-order growth, tilt stability, and metric regularity of the subdifferential D Drusvyatskiy, BS Mordukhovich, TTA Nghia arXiv preprint arXiv:1304.7385, 2013 | 59 | 2013 |
| The nonsmooth landscape of phase retrieval D Davis, D Drusvyatskiy, C Paquette IMA Journal of Numerical Analysis 40 (4), 2652-2695, 2020 | 53 | 2020 |
| An optimal first order method based on optimal quadratic averaging D Drusvyatskiy, M Fazel, S Roy SIAM Journal on Optimization 28 (1), 251-271, 2018 | 52 | 2018 |
| Level-set methods for convex optimization AY Aravkin, JV Burke, D Drusvyatskiy, MP Friedlander, S Roy Mathematical Programming 174 (1), 359-390, 2019 | 48 | 2019 |
| The many faces of degeneracy in conic optimization D Drusvyatskiy, H Wolkowicz arXiv preprint arXiv:1706.03705, 2017 | 46 | 2017 |
| Orthogonal invariance and identifiability A Daniilidis, D Drusvyatskiy, AS Lewis SIAM Journal on Matrix Analysis and Applications 35 (2), 580-598, 2014 | 42* | 2014 |
| Coordinate shadows of semidefinite and Euclidean distance matrices D Drusvyatskiy, G Pataki, H Wolkowicz SIAM Journal on Optimization 25 (2), 1160-1178, 2015 | 39 | 2015 |
| The proximal point method revisited D Drusvyatskiy arXiv preprint arXiv:1712.06038, 2017 | 33 | 2017 |
| Subgradient methods for sharp weakly convex functions D Davis, D Drusvyatskiy, KJ MacPhee, C Paquette Journal of Optimization Theory and Applications 179 (3), 962-982, 2018 | 32 | 2018 |
| Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence V Charisopoulos, Y Chen, D Davis, M Díaz, L Ding, D Drusvyatskiy Foundations of Computational Mathematics, 1-89, 2021 | 31 | 2021 |
| Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria D Drusvyatskiy, AD Ioffe, AS Lewis Mathematical Programming, 1-27, 2019 | 28 | 2019 |
| Noisy Euclidean distance realization: robust facial reduction and the Pareto frontier D Drusvyatskiy, N Krislock, YL Voronin, H Wolkowicz SIAM Journal on Optimization 27 (4), 2301-2331, 2017 | 28* | 2017 |
| Quadratic growth and critical point stability of semi-algebraic functions D Drusvyatskiy, AD Ioffe Mathematical Programming 153 (2), 635-653, 2015 | 25 | 2015 |
