Algorithmic (Semi-)Conjugacy via Koopman Operator Theory

09/14/2022
by   William T Redman, et al.
0

Iterative algorithms are of utmost importance in decision and control. With an ever growing number of algorithms being developed, distributed, and proprietarized, there is a similarly growing need for methods that can provide classification and comparison. By viewing iterative algorithms as discrete-time dynamical systems, we leverage Koopman operator theory to identify (semi-)conjugacies between algorithms using their spectral properties. This provides a general framework with which to classify and compare algorithms.

READ FULL TEXT
research
02/24/2021

Modern Koopman Theory for Dynamical Systems

The field of dynamical systems is being transformed by the mathematical ...
research
07/25/2019

On the Koopman operator of algorithms

A systematic mathematical framework for the study of numerical algorithm...
research
08/28/2019

Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective

Attempts from different disciplines to provide a fundamental understandi...
research
10/04/2018

Convergence of the Expectation-Maximization Algorithm Through Discrete-Time Lyapunov Stability Theory

In this paper, we propose a dynamical systems perspective of the Expecta...
research
07/11/2012

Predictive State Representations: A New Theory for Modeling Dynamical Systems

Modeling dynamical systems, both for control purposes and to make predic...
research
06/03/2020

Optimizing Neural Networks via Koopman Operator Theory

Koopman operator theory, a powerful framework for discovering the underl...
research
07/04/2018

Proximal algorithms for large-scale statistical modeling and optimal sensor/actuator selection

Several problems in modeling and control of stochastically-driven dynami...

Please sign up or login with your details

Forgot password? Click here to reset