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math.OC2021
Frequency-Domain Representation of First-Order Methods: A Simple and Robust Framework of Analysis
Ioannis Anagnostides, Ioannis Panageas
Motivated by recent applications in min-max optimization, we employ tools from nonlinear control theory in order to analyze a class of "historical" gradient-based methods, for whic…
math.OC2020
A Robust Framework for Analyzing Gradient-Based Dynamics in Bilinear Games
Ioannis Anagnostides, Paolo Penna
In this work, we establish a frequency-domain framework for analyzing gradient-based algorithms in linear minimax optimization problems; specifically, our approach is based on the…
math.OC2020
Solving Zero-Sum Games through Alternating Projections
Ioannis Anagnostides, Paolo Penna
In this work, we establish near-linear and strong convergence for a natural first-order iterative algorithm that simulates Von Neumann's Alternating Projections method in zero-sum…