Title: Distributed Averaging, Infinite Flow Theory, and Random Adaptation
Abstract: This talk explores averaging dynamics, a fundamental mechanism underlying many distributed algorithms, including distributed optimization, sensing, and learning. I present the infinite flow theory framework for analyzing averaging dynamics and highlight our developments in this area - particularly an extension of the Perron-Frobenius theorem for sequences of row-stochastic matrices. I will also introduce a lifting for the averaging dynamics, referred to as random adaptation dynamics, and examine key properties of the original dynamics through this lens.
Location & Time: CSL B02, November 12, 3-4PM
Reception in CSL 154 at 2:30PM
