Cluster algebraic interpretation of generalized Markov numbers and their matrixizations
Esther Banaian, Yasuaki Gyoda

TL;DR
This paper explores the cluster algebraic structure of generalized Markov numbers, introduces matrix families related to these structures, and connects them to combinatorial objects like posets and Christoffel words, providing new formulas and classifications.
Contribution
It introduces two matrix families associated with generalized Markov cluster algebras, classifies them, and links cluster variables to combinatorial weight-generating functions.
Findings
Matrix formulas relate to classical Markov matrices and cluster algebra matrices.
Explicit families of matrices are constructed and classified.
Cluster variables are realized as weight-generating functions of certain posets.
Abstract
Markov numbers, i.e. positive integers appearing in solutions to , can be viewed as specializations of cluster variables. The second author and Matsushita gave a generalization of the Markov equation, , whose solutions can be viewed as specializations of cluster variables in generalized cluster algebras. We give two families of matrices in associated to these cluster structures. These matrix formulas relate to previous matrices appearing in the context of Markov numbers, including Cohn matrices and generalized Cohn matrices given by the second author, Maruyama, and Sato, as well as matrices appearing in the context of cluster algebras, including matrix formulas given by Kanatarc{\i} O\u{g}uz and Y{\i}ld{\i}r{\i}m. We provide a classification of the two…
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Taxonomy
TopicsGraph theory and applications · Matrix Theory and Algorithms · Advanced Algebra and Logic
