Showing 2024Show all
3 papers · 1 filter
math.OC2024
Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence Rates
Daniil Vankov, Angelia Nedich, Lalitha Sankar
This paper focuses on solving a stochastic variational inequality (SVI) problem under relaxed smoothness assumption for a class of structured non-monotone operators. The SVI proble…
math.OC2024
Optimizing -Smooth Functions by Gradient Methods
Daniil Vankov, Anton Rodomanov, Angelia Nedich +2
We study gradient methods for optimizing -smooth functions, a class that generalizes Lipschitz-smooth functions and has gained attention for its relevance in machine le…
math.OC2024
Adaptive Methods for Variational Inequalities under Relaxed Smoothness Assumption
Daniil Vankov, Angelia Nedich, Lalitha Sankar
Variational Inequality (VI) problems have attracted great interest in the machine learning (ML) community due to their application in adversarial and multi-agent training. Despite…