15 citations · 15 across the 1 of their papers we have counts for
7 papers
Tight last-iterate convergence rates for no-regret learning in multi-player games
Noah Golowich, Sarath Pattathil, Constantinos Daskalakis
We study the question of obtaining last-iterate convergence rates for no-regret learning algorithms in multi-player games. We show that the optimistic gradient (OG) algorithm with…
An Optimal Multistage Stochastic Gradient Method for Minimax Problems
Alireza Fallah, Asuman Ozdaglar, Sarath Pattathil
In this paper, we study the minimax optimization problem in the smooth and strongly convex-strongly concave setting when we have access to noisy estimates of gradients. In particul…
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
Noah Golowich, Sarath Pattathil, Constantinos Daskalakis +1
In this paper we study the smooth convex-concave saddle point problem. Specifically, we analyze the last iterate convergence properties of the Extragradient (EG) algorithm. It is w…
A Decentralized Proximal Point-type Method for Saddle Point Problems
Weijie Liu, Aryan Mokhtari, Asuman Ozdaglar +3
In this paper, we focus on solving a class of constrained non-convex non-concave saddle point problems in a decentralized manner by a group of nodes in a network. Specifically, we…
Convergence Rate of for Optimistic Gradient and Extra-gradient Methods in Smooth Convex-Concave Saddle Point Problems
Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil
We study the iteration complexity of the optimistic gradient descent-ascent (OGDA) method and the extra-gradient (EG) method for finding a saddle point of a convex-concave unconstr…
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil
In this paper we consider solving saddle point problems using two variants of Gradient Descent-Ascent algorithms, Extra-gradient (EG) and Optimistic Gradient Descent Ascent (OGDA)…