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20142026
most citedLyapunov Approach to Consensus Problems

5 citations · 9 across the 12 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

math.OC2023

Huber Loss-Based Penalty Approach to Problems with Linear Constraints

Angelia Nedich, Tatiana Tatarenko

We consider a convex optimization problem with many linear inequality constraints. To deal with a large number of constraints, we provide a penalty reformulation of the problem, wh…

math.OC2023

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…

math.OC2023

Distributed Stochastic Optimization with Gradient Tracking over Time-Varying Directed Networks

Duong Thuy Anh Nguyen, Duong Tung Nguyen, Angelia Nedich

We study a distributed method called SAB-TV, which employs gradient tracking to collaboratively minimize the sum of smooth and strongly-convex local cost functions for networked ag…

cs.GT2023

CrowdCache: A Decentralized Game-Theoretic Framework for Mobile Edge Content Sharing

Duong Thuy Anh Nguyen, Jiaming Cheng, Duong Tung Nguyen +1

Mobile edge computing (MEC) is a promising solution for enhancing the user experience, minimizing content delivery expenses, and reducing backhaul traffic. In this paper, we propos…

cs.GT2023

Geometric Convergence of Distributed Heavy-Ball Nash Equilibrium Algorithm over Time-Varying Digraphs with Unconstrained Actions

Duong Thuy Anh Nguyen, Duong Tung Nguyen, Angelia Nedich

This paper presents a new distributed algorithm that leverages heavy-ball momentum and a consensus-based gradient method to find a Nash equilibrium (NE) in a class of non-cooperati…

math.OC2023

Accelerated /Push-Pull Methods for Distributed Optimization over Time-Varying Directed Networks

Duong Thuy Anh Nguyen, Duong Tung Nguyen, Angelia Nedich

This paper investigates a novel approach for solving the distributed optimization problem in which multiple agents collaborate to find the global decision that minimizes the sum of…