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

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 probabi...

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Published in:arXiv.org
Main Authors: Hartarsky, Ivailo, Tamás Róbert Mezei
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 30.11.2019
Subjects:
Algorithms
Cellular automata
Combinatorial analysis
Percolation
Two dimensional models
Upper bounds
ISSN:2331-8422
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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 Bollobás, 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, Bollobás, Morris and Smith on uncomputability in higher dimensions. The proof of NP-hardness is achieved by a technical reduction to the Set Cover problem.
AbstractList 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 Bollobás, 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, Bollobás, Morris and Smith on uncomputability in higher dimensions. The proof of NP-hardness is achieved by a technical reduction to the Set Cover problem.
Author Hartarsky, Ivailo
Tamás Róbert Mezei
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Cellular automata
Combinatorial analysis
Percolation
Two dimensional models
Upper bounds
Title Complexity of 2D bootstrap percolation difficulty: Algorithm and NP-hardness
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