Author

A. Gasnikov

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Preprint Sep 2026

Gradient-Free Methods for Stochastic Convex Optimization with Stochastic Functional Constraints

We develop accelerated gradient-free methods for stochastic convex optimization with constraints defined by expectations. Our batched primal-dual sliding method uses two-point evaluations sharing a random sample and guarantees expected objective error and expected maximum constraint violation at most $\varepsilon$. It...

Vadim Abronin, A. Gasnikov, D. Dvinskikh · 0 citations

Preprint Sep 2026

Gradient-Free Methods for Stochastic Convex Optimization with Stochastic Functional Constraints

We establish the optimal query complexity of smooth convex quadratic optimization from exact function values. For dimension $d$, smoothness $L$, and minimizer radius $R$, the sharp rate at small relative error is $\Theta(d\min\{d,\sqrt{LR^2/\varepsilon}\})$. The lower bound has no logarithmic loss and holds even for ad...

Vadim Abronin, A. Gasnikov, D. Dvinskikh · 0 citations