Bad approximability, bounded ratios and Diophantine exponents

TL;DR

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Contribution

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Abstract

For a real $m\times n$ matrix $\pmb{\xi}$, we consider its sequence of best Diophantine approximation vectors $ \pmb{x}_i \in \mathbb{Z}^n, \, i =1,2,3, ... $, the sequences of its norms $X_i = \|\pmb{x}_i\|$ and the norms of remainders $L_i = \|\pmb{\xi}\pmb{x}_i\|$. It is known that, in the cases $m=1$, bad approximability of $\pmb{\xi}$ is equivalent to the boundedness of ratios $\frac{X_{i+1}}{X_i}$, while for $n=1$ bad approximability of $\pmb{\xi}$ is equivalent to the boundedness of ratios $ \frac{L_i}{L_{i+1}}$. Moreover, carefully constructed example show that in the cases $m=1$ and $n=1$ boundedness of ratios $ \frac{L_i}{L_{i+1}}$ and $\frac{X_{i+1}}{X_i}$ respectively (the order of ratios changed), does not imply bad approximability of $\pmb{\xi}$. In the present paper, we study the impact of the boundedness of ratios on Diophantine properties of $\pmb{\xi}$, in particular, what restrictions it gives for Diophantine exponents $\omega(\pmb{\xi})$ and $\hat{\omega}(\pmb{\xi})$. One of our particular results deals with the case $m=n=2$. We prove that for $2\times 2 $ matrices $\pmb{\xi}$ boundedness of both ratios $ \frac{X_{i+1}}{X_i}, \frac{L_i}{L_{i+1}} $ implies inequality $\hat{\omega}(\pmb{\xi})\le \frac{4}{3}$ and that this result is optimal. Our methods combine parametric geometry of numbers as well as more classical tools.