5 papers · 1 filter
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…
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…
Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
TaeHo Yoon, Jaeyeon Kim, Jaewook J. Suh +1
Recently, accelerated algorithms using the anchoring mechanism for minimax optimization and fixed-point problems have been proposed, and matching complexity lower bounds establish…