5 papers
Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization
TaeHo Yoon, Nicolas Loizou
Acceleration for deterministic root-finding problems has been extensively studied in recent years; specifically, the anchor-based, or Halpern-type methods achieve optimal convergen…
On Same-Sample and Independent-Sample Stochastic Extragradient for Monotone Variational Inequalities
TaeHo Yoon, Nicolas Loizou
We study stochastic extragradient (SEG) methods for solving monotone variational inequality problems (VIPs) over a feasible set. Although extragradient is a foundational algorithm…
H-invariance theory: A complete characterization of minimax optimal fixed-point algorithms
TaeHo Yoon, Ernest K. Ryu, Benjamin Grimmer
For nonexpansive fixed-point problems, Halpern's method with optimal parameters, its so-called H-dual algorithm, and in fact, an infinite family of algorithms containing them, all…
Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
TaeHo Yoon, Sayantan Choudhury, Nicolas Loizou
Traditional Federated Learning (FL) approaches assume collaborative clients with aligned objectives working towards a shared global model. However, in many real-world scenarios, cl…
Accelerated Minimax Algorithms Flock Together
TaeHo Yoon, Ernest K. Ryu
Several new accelerated methods in minimax optimization and fixed-point iterations have recently been discovered, and, interestingly, they rely on a mechanism distinct from Nestero…