A Classification of Permutation Polynomials through Some Linear Maps
Megha M. Kolhekar, Harish K. Pillai

TL;DR
This paper introduces a novel classification of permutation polynomials using linear maps and their eigenspaces, revealing structural insights and distribution patterns over finite fields, with applications to specific polynomial classes.
Contribution
It proposes a new classification framework for permutation polynomials based on eigenspaces of linear maps, connecting various known classes and analyzing their properties.
Findings
Permutation polynomials can be classified via eigenspaces of certain linear maps.
Several known classes of permutation polynomials fit into this eigenspace classification.
Permutation polynomials over _{p^2} are closed under compositional inverses.
Abstract
In this paper, we propose linear maps over the space of all polynomials in that map to itself, through their evaluation map. Properties of these linear maps throw up interesting connections with permutation polynomials. We study certain properties of these linear maps. We propose to classify permutation polynomials by identifying the generalized eigenspaces of these maps, where the permutation polynomials reside. As it turns out, several classes of permutation polynomials studied in literature neatly fall into classes defined using these linear maps. We characterize the shapes of permutation polynomials that appear in the various generalized eigenspaces of these linear maps. For the case of , these generalized eigenspaces provide a degree-wise distribution of polynomials (and therefore permutation polynomials) over . We show…
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Taxonomy
TopicsCoding theory and cryptography · Polynomial and algebraic computation · Algebraic Geometry and Number Theory
