AI & ML
Sum-rank analogues of Reed--Muller codes over Abelian Galois extensions
arXiv math.IT0 views
arXiv:2610.31358v1 Announce Type: new Abstract: Let $\mathbb L/\mathbb K$ be a finite Abelian Galois extension with a fixed decomposition $\mathrm{Gal}(\mathbb L/\mathbb K)=\langle\sigma_1\rangle\times\cdots\times\langle\sigma_m\rangle$. We show that the quotient of the multivariate skew polynomial ring $\mathbb L[x_1,\ldots,x_m;\sigma_1,\ldots,\sigma_m]$ by suitable central polynomials $H_1(x_1),\ldots,H_m(x_m)$ is isomorphic to a product of $t$ copies of $\mathrm{End}_{\mathbb K}(\mathbb L)$, so that one skew polynomial represents all blocks of a sum-rank metric space. The sum-rank weight of an element equals the rank of an associated Dickson matrix and of a generalized Moore matrix. Using this, we define sum-rank Reed--Muller-type codes and show that their dimension and minimum distance are given by the formulas for affine Cartesian codes with side lengths $\mathrm{deg}\,H_1,\ldots,\mathrm{deg}\,H_m$; the same holds for codes supported on lower sets. The family contains as special cases linearized Reed--Solomon codes, Gabidulin codes, rank-metric Reed--Muller codes, and affine Cartesian codes. Over finite fields, every linearized Reed--Muller code of Berardini and Caruso is, after a monomial change of variables, a monomial code in our framework. This gives the dimension of all these codes, and their exact minimum distance in every degree whenever the support is a lower set.
Source:https://arxiv.org/abs/2610.31358PreviousChordal Construction of Column-Twisted Reed--Solomon CodesNextHomomorphic Encryption with Coset Code Construction
Related posts

What Arconatese 3-4 Pontedera tells us about Serie D - Girone D
0dev.to Watercooler
Your AI agent got an HTTP error. Should it retry?
0dev.to Discuss
Is AI dangerous, or just misunderstood?
0dev.to Discuss
Why we price a developer tool per fix instead of per seat
0dev.to Productivity
My LLM-Powered Sidekick for Painless PR Descriptions
0dev.to Productivity
Reward-Adaptive Backtracking GFlowNet: A Technique Worth Understanding
0dev.to Productivity