Complexity of 2D bootstrap percolation difficulty: Algorithm and NP-hardness

TL;DR

This paper introduces the first algorithm to compute the difficulty in 2D bootstrap percolation models and proves that this computation is NP-hard, revealing computational complexity in understanding these cellular automata.

Contribution

It provides the first algorithm for calculating difficulty and establishes NP-hardness, highlighting computational challenges in bootstrap percolation analysis.

Findings

First algorithm for difficulty computation in 2D bootstrap percolation

Explicit upper bounds for difficulty are derived

Computing difficulty is proven NP-hard

Abstract

Bootstrap percolation is a class of cellular automata with random initial state. Two-dimensional bootstrap percolation models have three rough universality classes, the most studied being the `critical' one. For this class the scaling of the quantity of greatest interest -- the critical probability -- was determined by Bollob\'as, Duminil-Copin, Morris and Smith in terms of a simply defined combinatorial quantity called `difficulty', so the subject seemed closed up to finding sharper results. However, the computation of the difficulty, was never considered. In this paper we provide the first algorithm to determine this quantity, which is, surprisingly, not as easy as the definition leads to thinking. The proof also provides some explicit upper bounds, which are of use for bootstrap percolation. On the other hand, we also prove the negative result that computing the difficulty of a critical model is NP-hard. This two-dimensional picture contrasts with an upcoming result of Balister, Bollob\'as, Morris and Smith on uncomputability in higher dimensions. The proof of NP-hardness is achieved by a technical reduction to the Set Cover problem.