Sampling Permutations with Cell Probes Is Hard

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Authors: Yaroslav Alekseev, Mika Göös, Konstantin Myasnikov, Artur Riazanov, Dmitry Sokolov
Cited by: 0

Abstract

Suppose we are given an infinite sequence of input cells, each initialized with a uniform random symbol from [n]. How hard is it to output a sequence in [n]n that is close to a uniform random permutation? Viola (SICOMP 2020) conjectured that if each output cell is computed by making d probes to input cells, then d≥ω(1). Our main result shows that, in fact, d≥ (logn)Ω(1), which is tight up to the constant in the exponent. Our techniques also show that if the probes are nonadaptive, then d≥ nΩ(1), which is an exponential improvement over the previous nonadaptive lower bound due to Yu and Zhan (ITCS 2024). Our results also imply lower bounds against succinct data structures for storing permutations.

Fields of Study & Subjects

Random Matrices and Applications Genome Rearrangement Algorithms Machine Learning and Algorithms

Publication details

Pages
234–245
Publication date
9 June 2026