Fast Computation of Minimal Interpolation Bases in Popov Form for Arbitrary Shifts

TL;DR

This paper introduces an efficient algorithm for computing minimal interpolation bases in shifted Popov form for arbitrary shifts, significantly improving computational complexity and applicability in coding theory and security.

Contribution

It presents the first algorithm achieving optimal complexity for arbitrary shifts by ensuring bases are in shifted Popov form with manageable size.

Findings

Achieves $ ilde{O}(m^{ ext{w}-1} \sigma)$ complexity for arbitrary shifts.

Ensures all bases are in shifted Popov form with size $O(m \sigma)$.

Introduces a divide-and-conquer approach based on degree information.

Abstract

We compute minimal bases of solutions for a general interpolation problem, which encompasses Hermite-Pad\'e approximation and constrained multivariate interpolation, and has applications in coding theory and security. This problem asks to find univariate polynomial relations between $m$ vectors of size $\sigma$; these relations should have small degree with respect to an input degree shift. For an arbitrary shift, we propose an algorithm for the computation of an interpolation basis in shifted Popov normal form with a cost of $\mathcal{O}\tilde{~}(m^{\omega-1} \sigma)$ field operations, where $\omega$ is the exponent of matrix multiplication and the notation $\mathcal{O}\tilde{~}(\cdot)$ indicates that logarithmic terms are omitted. Earlier works, in the case of Hermite-Pad\'e approximation and in the general interpolation case, compute non-normalized bases. Since for arbitrary shifts such bases may have size $\Theta(m^2 \sigma)$, the cost bound $\mathcal{O}\tilde{~}(m^{\omega-1} \sigma)$ was feasible only with restrictive assumptions on the shift that ensure small output sizes. The question of handling arbitrary shifts with the same complexity bound was left open. To obtain the target cost for any shift, we strengthen the properties of the output bases, and of those obtained during the course of the algorithm: all the bases are computed in shifted Popov form, whose size is always $\mathcal{O}(m \sigma)$. Then, we design a divide-and-conquer scheme. We recursively reduce the initial interpolation problem to sub-problems with more convenient shifts by first computing information on the degrees of the intermediate bases.