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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 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…