A. Lobanov

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20222026

most citedRandomized gradient-free methods in convex optimization

16 citations · 36 across the 20 of their papers we have counts for

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math.OC2022★ 16 cited

Randomized gradient-free methods in convex optimization

Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky +3

This review presents modern gradient-free methods to solve convex optimization problems. By gradient-free methods, we mean those that use only (noisy) realizations of the objective…

math.OC2022★ 3 cited

Gradient-Free Federated Learning Methods with $l_{1}$ and $l_{2}$ -Randomization for Non-Smooth Convex Stochastic Optimization Problems

Aleksandr Lobanov, Belal Alashqar, Darina Dvinskikh +1

This paper studies non-smooth problems of convex stochastic optimization. Using the smoothing technique based on the replacement of the function value at the considered point by th…

Randomized gradient-free methods in convex optimization

Gradient-Free Federated Learning Methods with l1​ and l2​-Randomization for Non-Smooth Convex Stochastic Optimization Problems

Gradient-Free Federated Learning Methods with $l_{1}$ and $l_{2}$ -Randomization for Non-Smooth Convex Stochastic Optimization Problems