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pro vyhledávání: '"Novitskii, Vasilii"'
Autor:
Pichugin, Alexander, Pechin, Maksim, Beznosikov, Aleksandr, Novitskii, Vasilii, Gasnikov, Alexander
Variational inequalities are a universal optimization paradigm that incorporate classical minimization and saddle point problems. Nowadays more and more tasks require to consider stochastic formulations of optimization problems. In this paper, we pre
Externí odkaz:
http://arxiv.org/abs/2408.06728
Autor:
Gasnikov, Alexander, Novitskii, Anton, Novitskii, Vasilii, Abdukhakimov, Farshed, Kamzolov, Dmitry, Beznosikov, Aleksandr, Takáč, Martin, Dvurechensky, Pavel, Gu, Bin
Gradient-free/zeroth-order methods for black-box convex optimization have been extensively studied in the last decade with the main focus on oracle calls complexity. In this paper, besides the oracle complexity, we focus also on iteration complexity,
Externí odkaz:
http://arxiv.org/abs/2201.12289
In this paper, we analyze gradient-free methods with one-point feedback for stochastic saddle point problems $\min_{x}\max_{y} \varphi(x, y)$. For non-smooth and smooth cases, we present analysis in a general geometric setup with arbitrary Bregman di
Externí odkaz:
http://arxiv.org/abs/2103.00321
We consider $\beta$-smooth (satisfies the generalized Holder condition with parameter $\beta > 2$) stochastic convex optimization problem with zero-order one-point oracle. The best known result was arXiv:2006.07862: $\mathbb{E} \left[f(\overline{x}_N
Externí odkaz:
http://arxiv.org/abs/2101.03821
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