ResEntSG: Restoration entropy estimation for dynamical systems via Riemannian metric optimization

• Main Authors: Christoph Kawan, Sigurdur Freyr Hafstein, Peter Giesl
• Format: Article
• Language: English
• Published: Elsevier 2021-07-01
• Series: SoftwareX
• Subjects: Restoration entropy; Subgradient algorithm; Geodesic convexity; Riemannian metric; Dynamical systems; C++
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352711021000741

In the remote state estimation problem, an observer reconstructs the state of a dynamical system at a remote location, where no direct sensor measurements are available. The estimator only has access to information sent through a digital channel. The notion of restoration entropy provides a way to determine the smallest channel capacity above which an observer can be designed that observes the system without a degradation of the initial estimation error. In general, restoration entropy is hard to compute. We present a class library in C++, that estimates the restoration entropy of a given system by computing an adapted metric for the system. The library is simple to use and implements a version of the subgradient algorithm for geodesically convex functions to compute an optimal metric in a class of conformal metrics. Included in the software are three example systems to demonstrate the use and efficacy of the library.