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arXiv · 2609.28097

An Introduction to the Lagrange and Markov Spectra through the Lens of Generalized Markov Numbers

Abstract

This text is a self-contained expository survey of the Lagrange and Markov spectra, centered on a comprehensive exposition of Markov's theorem and its generalizations. Its purpose is to provide a systematic text for learning the theory, with detailed proofs and explanations of the connections among its arithmetic, combinatorial, and geometric descriptions. The necessary background in continued fractions, quadratic irrationals, binary quadratic forms, and bi-infinite sequences is developed step by step, followed by an exposition of generalized Markov numbers, fence posets, curve lengths, and generalized Cohn matrices. One goal of this exposition is to explain the formula connecting generalized Markov numbers with the two spectra. For nonnegative integer parameters $(k_1,k_2,k_3)$, a permutation $σ\in\mathfrak S_3$, and a fraction label $t\in\mathbb Q_{\geq0}\cup\{\infty\}$, let $m_t$ be the associated generalized Markov number and let $k_t=k_{i_t}$ be the parameter assigned to its position $i_t\in\{1,2,3\}$. The text explains the construction of an associated finite sequence $S(t)$ of positive integers and the identity \[ \mathcal L(α_{S(t)}) =\mathcal M(Q_{S(t)}) =\frac{\sqrt{((3+k_1+k_2+k_3)m_t-k_t)^2-4}}{m_t}, \] where $α_{S(t)}=[\overline{S(t)}]$ and $Q_{S(t)}=(x-α_{S(t)}y)(x-α'_{S(t)}y)$, with the prime denoting quadratic conjugation. Here $\mathcal L$ and $\mathcal M$ denote the Lagrange and Markov constants, respectively. The survey explains how this identity relates generalized discrete Markov spectra to the classical theory and how Markov's theorem is recovered when the parameters vanish. The account also includes boundary values arising from irrational slopes and generalizations of Frobenius's uniqueness conjecture, providing a unified perspective on the classical theorem and its extensions.

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BibTeXRIS

Yasuaki Gyoda. 2026-09-23. An Introduction to the Lagrange and Markov Spectra through the Lens of Generalized Markov Numbers. https://arxiv.org/abs/2609.28097

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