5 papers · 1 filter
DADA: Dual Averaging with Distance Adaptation
Mohammad Moshtaghifar, Anton Rodomanov, Daniil Vankov +1
We present a novel universal gradient method for solving convex optimization problems. Our algorithm, Dual Averaging with Distance Adaptation (DADA), is based on the classical sche…
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…
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…
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…
Last Iterate Convergence of Popov Method for Non-monotone Stochastic Variational Inequalities
Daniil Vankov, Angelia Nedich, Lalitha Sankar
This paper focuses on non-monotone stochastic variational inequalities (SVIs) that may not have a unique solution. A commonly used efficient algorithm to solve VIs is the Popov met…