# Stability in linear dynamical systems

## This Bodes well

The intersection of linear dynamical systems and stability of dynamic systems.

Related: detecting non-stationarity. There is not much content here because I spent 2 years working on it and am too traumatised to revisit it.

Informally, I am admitting as “stable” any dynamical system which does not explode super-polynomially fast; We can think of these as systems where if the system is not stationary then at least the rate of change might be.

Energy-preserving systems are a special case of this.

There are many problems I am interested in that touch upon this.

## Pole representations

In the univariate, discrete-time case, in discrete-time linear systems terms, these are systems that have no poles outside the unit circle, but might have poles on the unit circle. In continuous time it is about systems that have no poles with positive real part. For finitely realizable systems this boils down to tracking trigonometric roots, e.g. Megretski (2003).

In a multivariate context we might consider eigenvalues of the transfer matrix in a similar light.

van Handel (2017) for example mention the standard result that the eigenvalues of a symmetric matrix $$X$$ are the roots of the characteristic polynomial $$\chi(t)=\operatorname{det}(t I-X)$$ and, equivalently, the poles of the Stieltjes transform $$s(t):=\operatorname{Tr}\left[(t I-X)^{-1}\right]=\frac{d}{d t} \log \chi(t)$$

## Reparameterisation

We can use cunning reparameterisation to keep systems stable. This Betancourt podcast on Sarah Heaps’ paper on parameterising stationarity in vector auto regressions is deep and IMO points the way to some other neat tricks in neural nets. She constructs interesting priors for this case, using some reparametrisations by Ansley and Kohn (1986).

Maybe related: Roy, Mcelroy, and Linton (2019)

TBC.