77 citations · 318 across the 57 of their papers we have counts for
6 papers · 1 filter
A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions
Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee +2
Zhang et al. introduced a novel modification of Goldstein's classical subgradient method, with an efficiency guarantee of for minimizing Lipschitz functions.…
Improved Rates for Derivative Free Gradient Play in Strongly Monotone Games
Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J Ratliff
The influential work of Bravo et al. 2018 shows that derivative free play in strongly monotone games has complexity , where is the target accura…
Active manifolds, stratifications, and convergence to local minima in nonsmooth optimization
Damek Davis, Dmitriy Drusvyatskiy, Liwei Jiang
We show that the subgradient method converges only to local minimizers when applied to generic Lipschitz continuous and subdifferentially regular functions that are definable in an…
Stochastic Optimization under Distributional Drift
Joshua Cutler, Dmitriy Drusvyatskiy, Zaid Harchaoui
We consider the problem of minimizing a convex function that is evolving according to unknown and possibly stochastic dynamics, which may depend jointly on time and on the decision…
Escaping strict saddle points of the Moreau envelope in nonsmooth optimization
Damek Davis, Mateo Díaz, Dmitriy Drusvyatskiy
Recent work has shown that stochastically perturbed gradient methods can efficiently escape strict saddle points of smooth functions. We extend this body of work to nonsmooth optim…
Conservative and semismooth derivatives are equivalent for semialgebraic maps
Damek Davis, Dmitriy Drusvyatskiy
Subgradient and Newton algorithms for nonsmooth optimization require generalized derivatives to satisfy subtle approximation properties: conservativity for the former and semismoot…