The maximum-average subtensor problem: equilibrium and out-of-equilibrium properties

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Main Authors: Erba, Vittorio, Kupferschmid, Nathan Malo, Ortiz, Rodrigo Pérez, Zdeborová, Lenka
Format: Preprint
Published: 2025
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author Erba, Vittorio
Kupferschmid, Nathan Malo
Ortiz, Rodrigo Pérez
Zdeborová, Lenka
author_facet Erba, Vittorio
Kupferschmid, Nathan Malo
Ortiz, Rodrigo Pérez
Zdeborová, Lenka
contents In this paper we introduce and study the Maximum-Average Subtensor ($p$-MAS) problem, in which one wants to find a subtensor of size $k$ of a given random tensor of size $N$, both of order $p$, with maximum sum of entries. We are motivated by recent work on the matrix case of the problem in which several equilibrium and non-equilibrium properties have been characterized analytically in the asymptotic regime $1 \ll k \ll N$, and a puzzling phenomenon was observed involving the coexistence of a clustered equilibrium phase and an efficient algorithm which produces submatrices in this phase. Here we extend previous results on equilibrium and algorithmic properties for the matrix case to the tensor case. We show that the tensor case has a similar equilibrium phase diagram as the matrix case, and an overall similar phenomenology for the considered algorithms. Additionally, we consider out-of-equilibrium landscape properties using Overlap Gap Properties and Franz-Parisi analysis, and discuss the implications or lack-thereof for average-case algorithmic hardness.
format Preprint
id arxiv_https___arxiv_org_abs_2506_15400
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle The maximum-average subtensor problem: equilibrium and out-of-equilibrium properties
Erba, Vittorio
Kupferschmid, Nathan Malo
Ortiz, Rodrigo Pérez
Zdeborová, Lenka
Disordered Systems and Neural Networks
Information Theory
Probability
In this paper we introduce and study the Maximum-Average Subtensor ($p$-MAS) problem, in which one wants to find a subtensor of size $k$ of a given random tensor of size $N$, both of order $p$, with maximum sum of entries. We are motivated by recent work on the matrix case of the problem in which several equilibrium and non-equilibrium properties have been characterized analytically in the asymptotic regime $1 \ll k \ll N$, and a puzzling phenomenon was observed involving the coexistence of a clustered equilibrium phase and an efficient algorithm which produces submatrices in this phase. Here we extend previous results on equilibrium and algorithmic properties for the matrix case to the tensor case. We show that the tensor case has a similar equilibrium phase diagram as the matrix case, and an overall similar phenomenology for the considered algorithms. Additionally, we consider out-of-equilibrium landscape properties using Overlap Gap Properties and Franz-Parisi analysis, and discuss the implications or lack-thereof for average-case algorithmic hardness.
title The maximum-average subtensor problem: equilibrium and out-of-equilibrium properties
topic Disordered Systems and Neural Networks
Information Theory
Probability
url https://arxiv.org/abs/2506.15400