# Effective estimates on the top Lyapunov exponent for random matrix products

## TL;DR

This paper introduces an efficient algorithm to estimate the top Lyapunov exponent for random products of positive 2x2 matrices, simplifying previous methods and providing explicit error bounds based on matrix properties.

## Contribution

It presents a simplified computational approach for Lyapunov exponents that relies only on eigenvalues of matrix products, improving upon earlier Fredholm-based algorithms.

## Key findings

- Algorithm computes Lyapunov exponents efficiently.
- Provides explicit error bounds based on matrix properties.
- Simplifies previous Fredholm determinant methods.

## Abstract

We study the top Lyapunov exponents of random products of positive $2 \times 2$ matrices and obtain an efficient algorithm for its computation. As in the earlier work of Pollicott, the algorithm is based on the Fredholm theory of determinants of trace-class linear operators. In this article we obtain a simpler expression for the approximations which only require calculation of the eigenvalues of finite matrix products and not the eigenvectors. Moreover, we obtain effective bounds on the error term in terms of two explicit constants: a constant which describes how far the set of matrices are from all being column stochastic, and a constant which measures the minimal amount of projective contraction of the positive quadrant under the action of the matrices.