Sub-Linear Root Detection, and New Hardness Results, for Sparse Polynomials Over Finite Fields

TL;DR

This paper introduces a deterministic sub-linear algorithm for detecting roots of sparse univariate polynomials over finite fields, with implications for polynomial factorization and complexity theory.

Contribution

It presents the first sub-linear complexity algorithm for root detection of sparse polynomials over finite fields and establishes new hardness results for related problems.

Findings

Deterministic sub-linear root detection algorithm for t-nomials in F_q[x]

Roots can be partitioned into a bounded number of cosets of subgroups

NP-hardness results for several polynomial problems when t is not fixed

Abstract

We present a deterministic 2^O(t)q^{(t-2)(t-1)+o(1)} algorithm to decide whether a univariate polynomial f, with exactly t monomial terms and degree <q, has a root in F_q. A corollary of our method --- the first with complexity sub-linear in q when t is fixed --- is that the nonzero roots in F_q can be partitioned into at most 2 \sqrt{t-1} (q-1)^{(t-2)(t-1)} cosets of two subgroups S_1,S_2 of F^*_q, with S_1 in S_2. Another corollary is the first deterministic sub-linear algorithm for detecting common degree one factors of k-tuples of t-nomials in F_q[x] when k and t are fixed. When t is not fixed we show that each of the following problems is NP-hard with respect to BPP-reductions, even when p is prime: (1) detecting roots in F_p for f, (2) deciding whether the square of a degree one polynomial in F_p[x] divides f, (3) deciding whether the discriminant of f vanishes, (4) deciding whether the gcd of two t-nomials in F_p[x] has positive degree. Finally, we prove that if the complexity of root detection is sub-linear (in a refined sense), relative to the straight-line program encoding, then NEXP is not in P/Poly.