On the robust hardness of Gr\"obner basis computation

TL;DR

This paper demonstrates that computing Gr"obner bases remains NP-hard even when approximations are allowed, indicating the problem's robustness in computational hardness even for simple polynomial systems.

Contribution

The paper establishes the robust NP-hardness of approximate Gr"obner basis computation, extending hardness results to simplified polynomial systems and strengthening existing complexity bounds.

Findings

NP-hardness persists with approximate algorithms

Robust hardness applies to simple polynomial systems

Connections to SAT variants and graph-coloring problems

Abstract

The computation of Gr\"obner bases is an established hard problem. By contrast with many other problems, however, there has been little investigation of whether this hardness is robust. In this paper, we frame and present results on the problem of approximate computation of Gr\"obner bases. We show that it is NP-hard to construct a Gr\"obner basis of the ideal generated by a set of polynomials, even when the algorithm is allowed to discard a $(1 - \epsilon)$ fraction of the generators, and likewise when the algorithm is allowed to discard variables (and the generators containing them). Our results shows that computation of Gr\"obner bases is robustly hard even for simple polynomial systems (e.g. maximum degree 2, with at most 3 variables per generator). We conclude by greatly strengthening results for the Strong $c$-Partial Gr\"obner problem posed by De Loera et al. Our proofs also establish interesting connections between the robust hardness of Gr\"obner bases and that of SAT variants and graph-coloring.