Decoding Multivariate Multiplicity Codes on Product Sets

TL;DR

This paper presents a polynomial-time decoding algorithm for multivariate multiplicity codes over large product sets, surpassing traditional bounds and enabling decoding beyond the Johnson bound.

Contribution

It introduces a novel decoding approach that directly applies the polynomial method to multivariate codes without reduction, achieving decoding up to the code's distance.

Findings

Decoding algorithm works over finite product sets and fields of large or zero characteristic.

Algorithm exceeds both the unique decoding and Johnson bounds.

Polynomial bound on list size is established through a new multivariate perspective.

Abstract

The multiplicity Schwartz-Zippel lemma bounds the total multiplicity of zeroes of a multivariate polynomial on a product set. This lemma motivates the multiplicity codes of Kopparty, Saraf and Yekhanin [J. ACM, 2014], who showed how to use this lemma to construct high-rate locally-decodable codes. However, the algorithmic results about these codes crucially rely on the fact that the polynomials are evaluated on a vector space and not an arbitrary product set. In this work, we show how to decode multivariate multiplicity codes of large multiplicities in polynomial time over finite product sets (over fields of large characteristic and zero characteristic). Previously such decoding algorithms were not known even for a positive fraction of errors. In contrast, our work goes all the way to the distance of the code and in particular exceeds both the unique decoding bound and the Johnson bound. For errors exceeding the Johnson bound, even combinatorial list-decodablity of these codes was not known. Our algorithm is an application of the classical polynomial method directly to the multivariate setting. In particular, we do not rely on a reduction from the multivariate to the univariate case as is typical of many of the existing results on decoding codes based on multivariate polynomials. However, a vanilla application of the polynomial method in the multivariate setting does not yield a polynomial upper bound on the list size. We obtain a polynomial bound on the list size by taking an alternative view of multivariate multiplicity codes. In this view, we glue all the partial derivatives of the same order together using a fresh set $z$ of variables. We then apply the polynomial method by viewing this as a problem over the field $\mathbb{F}(z)$ of rational functions in $z$.