9 citations · 31 across the 31 of their papers we have counts for
35 papers · 1 filter
On the Sampling-based Computation of Nash Equilibria under Uncertainty via the Nikaido-Isoda Function
Luke Marrinan, Farzad Yousefian, Uday V. Shanbhag
We consider the computation of an equilibrium of a stochastic Nash equilibrium problem, where the player objectives are assumed to be -Lipschitz continuous and convex given ri…
On the Resolution of Stochastic MPECs over Networks: Distributed Implicit Zeroth-Order Gradient Tracking Methods
Mohammadjavad Ebrahimi, Uday V. Shanbhag, Farzad Yousefian
The mathematical program with equilibrium constraints (MPEC) is a powerful yet challenging class of constrained optimization problems, where the constraints are characterized by a…
On iteratively regularized first-order methods for simple bilevel optimization
Sepideh Samadi, Daniel Burbano, Farzad Yousefian
We consider simple bilevel optimization (SBO) problems where the goal is to compute among the optimal solutions of a composite convex optimization problem, one that minimizes a sec…
A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning
Yuyang Qiu, Kibaek Kim, Farzad Yousefian
Heterogeneity in federated learning (FL) is a critical and challenging aspect that significantly impacts model performance and convergence. In this paper, we propose a novel framew…
Self-tuned Regularized Federated Methods with Guarantees for Optimal Solution Selection
Mohammadjavad Ebrahimi, Yuyang Qiu, Shisheng Cui +1
We study a hierarchical federated learning (FL) problem, where clients cooperatively seek to select among multiple optimal solutions of a primary distributed learning problem, a so…
Iteratively Regularized Gradient Tracking Methods for Optimal Equilibrium Seeking
Yuyang Qiu, Farzad Yousefian, Brian Zhang
In noncooperative Nash games, equilibria are often inefficient. This is exemplified by the Prisoner's Dilemma and was first provably shown in the 1980s. Since then, understanding t…