5 citations · 9 across the 12 of their papers we have counts for
4 papers · 2 filters
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…
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…
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…
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…