  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.
