Output-sensitive Sparse Polynomial GCD over Finite Fields is NP-hard
This resolves an open problem (Challenge 5) in the sparsity challenges, establishing a fundamental hardness result for sparse polynomial GCD computation over finite fields.
The paper proves that output-sensitive sparse polynomial GCD computation over finite fields is NP-hard under BPP reduction, meaning no randomized polynomial-time algorithm exists unless NP is in BPP. It also shows that the Roots of Unity Detection problem over finite fields is NP-hard.
In this paper, we prove that output-sensitive sparse polynomial GCD computation over finite fields is NP-hard under BPP many-one reduction. More precisely, for two sparse univariate polynomials $f,g$ with finite field coefficients, there exists no randomized algorithm to compute $\mathrm{gcd}(f,g)$, which is polynomial-time in the sizes of $f,g,\gcd(f,g)$ under the standard complexity assumption $\mathrm{NP}\nsubseteq\mathrm{BPP}$. This settles the open problem posed as Challenge 5 in The Sparsity Challenges in the finite field setting. Furthermore, we show that the Roots of Unity Detection problem over finite fields is NP-hard; that is, determining whether the GCD of a sparse univariate polynomial and $x^n - 1$ has nonzero degree is NP-hard.