The Complexity of Divisibility

TL;DR

This paper investigates the computational complexity of divisibility problems in probability theory, establishing NP-completeness for stochastic maps and a complexity hierarchy for probability distributions, with practical algorithms for some cases.

Contribution

It proves NP-completeness for finite divisibility of stochastic maps and extends results to quantum maps, while also establishing a complexity hierarchy for distribution divisibility and decomposability.

Findings

Finite divisibility of stochastic maps is NP-complete.

Distribution divisibility is in P, but decomposability is NP-hard.

Explicit polynomial-time algorithm for distribution divisibility.

Abstract

We address two sets of long-standing open questions in probability theory, from a computational complexity perspective: divisibility of stochastic maps, and divisibility and decomposability of probability distributions. We prove that finite divisibility of stochastic maps is an NP-complete problem, and extend this result to nonnegative matrices, and completely-positive trace-preserving maps, i.e. the quantum analogue of stochastic maps. We further prove a complexity hierarchy for the divisibility and decomposability of probability distributions, showing that finite distribution divisibility is in P, but decomposability is NP-hard. For the former, we give an explicit polynomial-time algorithm. All results on distributions extend to weak-membership formulations, proving that the complexity of these problems is robust to perturbations.