1 citations · 1 across the 2 of their papers we have counts for
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Frank-Wolfe Algorithms for (L0, L1)-smooth functions
A. A. Vyguzov, F. S. Stonyakin
We propose a new version of the Frank-Wolfe method, called the (L0, L1)-Frank-Wolfe algorithm, developed for optimization problems with (L0, L1)-smooth objectives. We establish tha…
A Fully Adaptive Frank-Wolfe Algorithm for Relatively Smooth Problems and Its Application to Centralized Distributed Optimization
A. A. Vyguzov, F. S. Stonyakin
We study the Frank-Wolfe algorithm for constrained optimization problems with relatively smooth objectives. Building upon our previous work, we propose a fully adaptive variant of…
Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems
O. S. Savchuk, M. S. Alkousa, A. S. Shushko +4
In this paper, we propose some accelerated methods for solving optimization problems under the condition of relatively smooth and relatively Lipschitz continuous functions with an…
Adaptive Variant of Frank-Wolfe Method for Relative Smooth Convex Optimization Problems
Alexander Vyguzov, Fedor Stonyakin
The paper introduces a new adaptive version of the Frank-Wolfe algorithm for relatively smooth convex functions. It is proposed to use the Bregman divergence other than half the sq…
Subgradient methods with variants of Polyak step-size for quasi-convex optimization with inequality constraints for analogues of sharp minima
S. M. Puchinin, E. R. Korolkov, F. S. Stonyakin +2
In this paper, we consider two variants of the concept of sharp minimum for mathematical programming problems with quasiconvex objective function and inequality constraints. It inv…