Effective Gaps between singular values of non-stationary matrix products subject to non-degenerate noise
Sam Bednarski, Jonathan DeWitt, Anthony Quas

TL;DR
This paper investigates the behavior of singular values and Lyapunov exponents in non-stationary random matrix products with small additive noise, establishing universal gap estimates that grow linearly with the number of matrices.
Contribution
It extends recent entropy-based methods to provide quantitative and fibered estimates for singular value gaps in non-stationary matrix products under noise.
Findings
Gaps between singular values grow at least linearly with noise and number of matrices.
Extended methods to estimate gaps between arbitrary consecutive exponents.
Provided universal estimates applicable to non-stationary matrix products.
Abstract
We study the singular values and Lyapunov exponents of non-stationary random matrix products subject to small, absolutely continuous, additive noise. Consider a fixed sequence of matrices of bounded norm. Independently perturb the matrices by additive noise distributed according to Lebesgue measure on matrices with norm less than . Then the gaps between the logarithms of the singular values of the random product of of these matrices are all of order at least , both in expectation; and almost surely for large . To prove this, we develop recent work of Gorodetski and Kleptsyn \cite{gorodetski2023nonstationary}. That paper gives a very flexible method, based on relative entropy, for showing that a non-stationary product of matrices in SL(d,R) has a strictly positive Lyapunov exponent. We extend their work in two ways, firstly by making the estimates…
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Taxonomy
TopicsStatistical and numerical algorithms · Spectral Theory in Mathematical Physics · Mathematical Approximation and Integration
