5 citations · 9 across the 12 of their papers we have counts for
13 papers
A Certificate of Unboundedness for Polynomial Optimization Problems
Rohan Rele, Angelia Nedich
Global polynomial optimization methods typically rely on compactness of the feasible region in order to find solutions. These methods can incur considerable computational expense a…
Fast Distributed Nash Equilibrium Seeking in Monotone Games
Tatiana Tatarenko, Angelia Nedich
This work proposes a novel distributed approach for computing a Nash equilibrium in convex games with merely monotone and restricted strongly monotone pseudo-gradients. By leveragi…
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…
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…