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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…
Non-Monotone Variational Inequalities
Sina Arefizadeh, Angelia Nedich
In this paper, we focus on deriving some sufficient conditions for the existence of solutions to non-monotone Variational Inequalities (VIs) based on inverse mapping theory. We hav…
On Characterizations of Potential and Ordinal Potential Games
Sina Arefizadeh, Angelia Nedich, Gautam Dasarathy
This paper investigates some necessary and sufficient conditions for a game to be a potential game. At first, we extend the classical results of Slade and Monderer and Shapley from…
On existence of solutions to non-convex minimization problems
Rohan Rele, Angelia Nedich
We provide a unified framework for a systematic analysis of the existence of solutions to general nonconvex problems, relying on asymptotic and retractive cones for functions and s…
Robust Constrained Consensus and Inequality-constrained Distributed Optimization with Guaranteed Differential Privacy and Accurate Convergence
Yongqiang Wang, Angelia Nedic
We address differential privacy for fully distributed optimization subject to a shared inequality constraint. By co-designing the distributed optimization mechanism and the differe…