Search Results for author: Rinat Kamalov

Found 1 papers, 0 papers with code

Tensor methods for strongly convex strongly concave saddle point problems and strongly monotone variational inequalities

no code implementations31 Dec 2020 Petr Ostroukhov, Rinat Kamalov, Pavel Dvurechensky, Alexander Gasnikov

The first method is based on the assumption of $p$-th order smoothness of the objective and it achieves a convergence rate of $O \left( \left( \frac{L_p R^{p - 1}}{\mu} \right)^\frac{2}{p + 1} \log \frac{\mu R^2}{\varepsilon_G} \right)$, where $R$ is an estimate of the initial distance to the solution, and $\varepsilon_G$ is the error in terms of duality gap.

Optimization and Control

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