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Sorting with Pattern-Avoiding Stacks: The 132-Machine

Sorting with pattern-avoiding stacks: the \(132\)-machine
Authors: Giulio Cerbai; Anders Claesson; Luca S. Ferrari; Einar Steingrímsson;

Sorting with Pattern-Avoiding Stacks: The 132-Machine

Abstract

This paper continues the analysis of the pattern-avoiding sorting machines recently introduced by Cerbai, Claesson and Ferrari (2020). These devices consist of two stacks, through which a permutation is passed in order to sort it, where the content of each stack must at all times avoid a certain pattern. Here we characterize and enumerate the set of permutations that can be sorted when the first stack is $132$-avoiding, solving one of the open problems proposed by the above mentioned authors. To that end we present several connections with other well known combinatorial objects, such as lattice paths and restricted growth functions (which encode set partitions). We also provide new proofs for the enumeration of some sets of pattern-avoiding restricted growth functions and we expect that the tools introduced can be fruitfully employed to get further similar results.

Country
Italy
Keywords

Permutations, words, matrices, 05A05, 05A15, 05A19, pattern-avoiding sorting machines, Exact enumeration problems, generating functions, pattern-avoiding permutation stack sorting enumeration restricted growth function lattice path, 004, Partitions of sets, FOS: Mathematics, Mathematics - Combinatorics, Combinatorics (math.CO), QA, Factorials, binomial coefficients, combinatorial functions, Searching and sorting, Mathematics, Combinatorial identities, bijective combinatorics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
gold