Author

A. Gasnikov

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Preprint Sep 2026

Application of Optimal Inexact Second-Order Acceleration to Distributed Stochastic Optimization under Statistical Similarity

We consider distributed stochastic convex optimization with a fixed budget of $N$ independent samples split among $m$ workers. Sample average approximation reduces the problem to a regularized finite-sum problem whose local Hessians are statistically similar. This allows the Hessian of the local objective at the server...

Yury A. Sokolov, Maxim Mashtaler, A. Gasnikov et al. · 0 citations

Preprint Sep 2026

Lower Bounds For Gradient-Free Convex Optimization And Convex-Concave Saddle-Point Problems

We study the query complexity of optimization with exact scalar-value information. For globally $L$-smooth convex functions on $\mathbb R^d$ with a minimizer in a Euclidean ball of radius $R$, we prove the lower bound $\Omega(d\min\{d,\sqrt{LR^2/\varepsilon}\})$ for adaptive randomized algorithms in the stated accuracy...

Yuriy Dorn, D. Dvinskikh, Т. В. Логінов et al. · 0 citations

Preprint Sep 2026

The Complexity of Convex Optimization with Mismatched Geometry

Optimal first-order methods on non-Euclidean domains such as the $\ell_1$ ball $B_1^n(R)=\{x\in\mathbb R^n:\|x\|_1\le R\}$ pair the prox-function with the norm in which smoothness is measured. When the gradient is $L$-Lipschitz in the Euclidean norm only, the accelerated method with a Euclidean prox-setup reduces the f...

Т. В. Логінов, A. Gasnikov, Yuriy Dorn et al. · 0 citations