Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization

Chen, Lesi

doi:10.48550/arxiv.2208.05925

Search citation statements

Order By: Relevance

Paper Sections

Select...

Convergence In Terms Of the Gradient Norm For Spp1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For smooth convex-concave saddle point problems an optimal algorithm with ‖∇ x,y f(x k , y k )‖ 2 proportional to k −1 was proposed in [122] (see also [30] and [71] for monotone inclusion). For the stochastic case, see [20,27,74].…”

Section: Convergence In Terms Of the Gradient Norm For Sppmentioning

confidence: 99%

Smooth monotone stochastic variational inequalities and saddle point problems: A survey

Beznosikov

Polyak²,

Gorbunov

et al. 2023

Eur. Math. Soc. Mag.

View full text Add to dashboard Cite

This paper is a survey of methods for solving smooth, (strongly) monotone stochastic variational inequalities. To begin with, we present the deterministic foundation from which the stochastic methods eventually evolved. Then we review methods for the general stochastic formulation, and look at the finite-sum setup. The last parts of the paper are devoted to various recent (not necessarily stochastic) advances in algorithms for variational inequalities.

show abstract