4 papers
Efficient Gradient Methods for Distributed Saddle Problems
Ruichen Luo, Anton Rodomanov, Sebastian U. Stich
The distributed setting for Saddle Problems (SPs) has recently emerged as a framework for various modern applications in machine learning and multiagent systems. Despite its releva…
Monotone Near-Zero-Sum Games: A Generalization of Convex-Concave Minimax
Ruichen Luo, Sebastian U. Stich, Krishnendu Chatterjee
Zero-sum and non-zero-sum (aka general-sum) games are relevant in a wide range of applications. While general non-zero-sum games are computationally hard, researchers focus on the…
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
Ruichen Luo, Sebastian U Stich, Samuel Horváth +1
LocalSGD and SCAFFOLD are widely used methods in distributed stochastic optimization, with numerous applications in machine learning, large-scale data processing, and federated lea…
Linear Equations with Min and Max Operators: Computational Complexity
Krishnendu Chatterjee, Ruichen Luo, Raimundo Saona +1
We consider a class of optimization problems defined by a system of linear equations with min and max operators. This class of optimization problems has been studied under restrict…