Pseudodeterministic Algorithms for Minimum Cut Problems

Fuente: arXiv
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Auteurs principaux: Agarwala, Aryan, Varma, Nithin
Format: Preprint
Publié: 2025
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author Agarwala, Aryan
Varma, Nithin
author_facet Agarwala, Aryan
Varma, Nithin
contents In this paper, we present efficient pseudodeterministic algorithms for both the global minimum cut and minimum s-t cut problems. The running time of our algorithm for the global minimum cut problem is asymptotically better than the fastest sequential deterministic global minimum cut algorithm (Henzinger, Li, Rao, Wang; SODA 2024). Furthermore, we implement our algorithm in sequential, streaming, PRAM, and cut-query models, where no efficient deterministic global minimum cut algorithms are known.
format Preprint
id arxiv_https___arxiv_org_abs_2512_23468
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Pseudodeterministic Algorithms for Minimum Cut Problems
Agarwala, Aryan
Varma, Nithin
Data Structures and Algorithms
Computational Complexity
In this paper, we present efficient pseudodeterministic algorithms for both the global minimum cut and minimum s-t cut problems. The running time of our algorithm for the global minimum cut problem is asymptotically better than the fastest sequential deterministic global minimum cut algorithm (Henzinger, Li, Rao, Wang; SODA 2024). Furthermore, we implement our algorithm in sequential, streaming, PRAM, and cut-query models, where no efficient deterministic global minimum cut algorithms are known.
title Pseudodeterministic Algorithms for Minimum Cut Problems
topic Data Structures and Algorithms
Computational Complexity
url https://arxiv.org/abs/2512.23468