# Fast approximation of the $p$-radius, matrix pressure or generalised Lyapunov exponent for positive and dominated matrices

## TL;DR

This paper introduces a new algorithm for efficiently approximating the p-radius, matrix pressure, or generalized Lyapunov exponent for positive or dominated matrices, with significant improvements for low-dimensional cases.

## Contribution

The authors develop a novel eigenvalue-based algorithm using Fredholm determinants to compute the p-radius for positive and dominated matrices, enhancing accuracy and efficiency.

## Key findings

- Significant improvements over existing methods for low-dimensional positive matrix pairs.
- The algorithm interprets the p-radius as a leading eigenvalue of a trace-class operator.
- Applicable to matrices with positivity or domination properties, relevant in wavelet and fractal analysis.

## Abstract

If A_1,...,A_N are real square matrices then the p-radius, generalised Lyapunov exponent or matrix pressure is defined to be the asymptotic exponential growth rate of the sum $\sum_{i_1,\ldots,i_n=1}^N \|A_{i_n}\cdots A_{i_1}\|^p$, where p is a real parameter. Under its various names this quantity has been investigated for its applications to topics including wavelet regularity and refinement equations, fractal geometry and the large deviations theory of random matrix products. In this article we present a new algorithm for computing the p-radius under the hypothesis that the matrices are all positive, or more generally under the hypothesis that they satisfy a weaker condition called domination. This algorithm is based on interpreting the p-radius as the leading eigenvalue of a trace-class operator on a Hilbert space and estimating that eigenvalue via approximations to the Fredholm determinant of the operator. In this respect our method is closely related to the work of Z.-Q. Bai and M. Pollicott on computing the top Lyapunov exponent of a random matrix product. For pairs of positive matrices of low dimension our method yields substantial improvements over existing methods.