114 citations · 664 across the 92 of their papers we have counts for
5 papers · 2 filters
Adaptive extra-gradient methods for min-max optimization and games
Kimon Antonakopoulos, E. Veronica Belmega, Panayotis Mertikopoulos
We present a new family of min-max optimization algorithms that automatically exploit the geometry of the gradient data observed at earlier iterations to perform more informative e…
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis +1
This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm's convergence properties in non-convex problems. We first show that the s…
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Ya-Ping Hsieh, Panayotis Mertikopoulos, Volkan Cevher
Compared to ordinary function minimization problems, min-max optimization algorithms encounter far greater challenges because of the existence of periodic cycles and similar phenom…
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
Owing to their stability and convergence speed, extragradient methods have become a staple for solving large-scale saddle-point problems in machine learning. The basic premise of t…
Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games
Tianyi Lin, Zhengyuan Zhou, Panayotis Mertikopoulos +1
In this paper, we consider multi-agent learning via online gradient descent in a class of games called -cocoercive games, a fairly broad class of games that admits many Nash equ…