Dmitriy Drusvyatskiy
Dmitriy Drusvyatskiy
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Cited by
Stochastic model-based minimization of weakly convex functions
D Davis, D Drusvyatskiy
SIAM Journal on Optimization 29 (1), 207–239, 2018
Error bounds, quadratic growth, and linear convergence of proximal methods
D Drusvyatskiy, AS Lewis
Mathematics of Operations Research 43 (3), 919-948, 2018
Stochastic subgradient method converges on tame functions
D Davis, D Drusvyatskiy, S Kakade, JD Lee
Foundations of computational mathematics 20 (1), 119-154, 2020
Efficiency of minimizing compositions of convex functions and smooth maps
D Drusvyatskiy, C Paquette
Mathematical Programming 178, 503-558, 2019
Transversality and alternating projections for nonconvex sets
D Drusvyatskiy, AD Ioffe, AS Lewis
Foundations of Computational Mathematics 15 (6), 1637-1651, 2015
Subgradient methods for sharp weakly convex functions
D Davis, D Drusvyatskiy, KJ MacPhee, C Paquette
Journal of Optimization Theory and Applications 179, 962-982, 2018
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
The nonsmooth landscape of phase retrieval
D Davis, D Drusvyatskiy, C Paquette
IMA Journal of Numerical Analysis 40 (4), 2652-2695, 2020
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
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 21 (6), 1505-1593, 2021
The many faces of degeneracy in conic optimization
D Drusvyatskiy, H Wolkowicz
Foundations and Trends® in Optimization 3 (2), 77-170, 2017
Second-order growth, tilt stability, and metric regularity of the subdifferential
D Drusvyatskiy, BS Mordukhovich, TTA Nghia
arXiv preprint arXiv:1304.7385, 2013
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
Level-set methods for convex optimization
AY Aravkin, JV Burke, D Drusvyatskiy, MP Friedlander, S Roy
Mathematical Programming 174, 359-390, 2019
Stochastic optimization with decision-dependent distributions
D Drusvyatskiy, L Xiao
Mathematics of Operations Research 48 (2), 954-998, 2023
The proximal point method revisited
D Drusvyatskiy
arXiv preprint arXiv:1712.06038, 2017
Nonsmooth optimization using Taylor-like models: error bounds, convergence, and termination criteria
D Drusvyatskiy, AD Ioffe, AS Lewis
Mathematical Programming 185, 357-383, 2021
Multiplayer performative prediction: Learning in decision-dependent games
A Narang, E Faulkner, D Drusvyatskiy, M Fazel, LJ Ratliff
Journal of Machine Learning Research 24 (202), 1-56, 2023
Curves of descent
D Drusvyatskiy, AD Ioffe, AS Lewis
SIAM Journal on Control and Optimization 53 (1), 114-138, 2015
From low probability to high confidence in stochastic convex optimization
D Davis, D Drusvyatskiy, L Xiao, J Zhang
Journal of machine learning research 22 (49), 1-38, 2021
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