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Cited by 258

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Stochastic Model-Based Minimization of Weakly Convex Functions

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Davis, Damek, Drusvyatskiy, Dmitriy·2019·SIAM Journal on Optimization

Abstract

We consider a family of algorithms that successively sample and minimize simple stochastic models of the objective function. We show that under reasonable conditions on approximation quality and regularity of the models, any such algorithm drives a natural stationarity measure to zero at the rate $O(k^{-1/4})$. As a consequence, we obtain the first complexity guarantees for the stochastic proximal point, proximal subgradient, and regularized Gauss--Newton methods for minimizing compositions of convex functions with smooth maps. The guiding principle, underlying the complexity guarantees, is that all algorithms under consideration can be interpreted as approximate descent methods on an implicit smoothing of the problem, given by the Moreau envelope. Specializing to classical circumstances, we obtain the long-sought convergence rate of the stochastic projected gradient method, without batching, for minimizing a smooth function on a closed convex set.

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Metadata

Authors
Davis, D., & Drusvyatskiy, D.
Venue
SIAM Journal on Optimization
Published
2019-01
Volume
29
Issue
1
Pages
207-239
ISSN
  • 1052-6234 (ISSN-L)
  • 1095-7189
OpenAlex
W2790417304
Cited by
258
References
40
FWCI
15.43
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Stochastic Model-Based Minimization of Weakly Convex Functions. https://app.cochat.ai/papers/10.1137/18m1178244
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Davis, D., & Drusvyatskiy, D. (2019). Stochastic Model-Based Minimization of Weakly Convex Functions. SIAM Journal on Optimization. https://doi.org/10.1137/18m1178244

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