Branch and Cut for Partitioning a Graph into a Cycle of Clusters

Fuente: arXiv
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Autores principales: Eifler, Leon, Witzig, Jakob, Gleixner, Ambros
Formato: Preprint
Publicado: 2024
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author Eifler, Leon
Witzig, Jakob
Gleixner, Ambros
author_facet Eifler, Leon
Witzig, Jakob
Gleixner, Ambros
contents In this paper we study formulations and algorithms for the cycle clustering problem, a partitioning problem over the vertex set of a directed graph with nonnegative arc weights that is used to identify cyclic behavior in simulation data generated from nonreversible Markov state models. Here, in addition to partitioning the vertices into a set of coherent clusters, the resulting clusters must be ordered into a cycle such as to maximize the total net flow in the forward direction of the cycle. We provide a problem-specific binary programming formulation and compare it to a formulation based on the reformulation-linearization technique (RLT). We present theoretical results on the polytope associated with our custom formulation and develop primal heuristics and separation routines for both formulations. In computational experiments on simulation data from biology we find that branch and cut based on the problem-specific formulation outperforms the one based on RLT.
format Preprint
id arxiv_https___arxiv_org_abs_2401_08412
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Branch and Cut for Partitioning a Graph into a Cycle of Clusters
Eifler, Leon
Witzig, Jakob
Gleixner, Ambros
Optimization and Control
65K05, 90C11, 60-xx
In this paper we study formulations and algorithms for the cycle clustering problem, a partitioning problem over the vertex set of a directed graph with nonnegative arc weights that is used to identify cyclic behavior in simulation data generated from nonreversible Markov state models. Here, in addition to partitioning the vertices into a set of coherent clusters, the resulting clusters must be ordered into a cycle such as to maximize the total net flow in the forward direction of the cycle. We provide a problem-specific binary programming formulation and compare it to a formulation based on the reformulation-linearization technique (RLT). We present theoretical results on the polytope associated with our custom formulation and develop primal heuristics and separation routines for both formulations. In computational experiments on simulation data from biology we find that branch and cut based on the problem-specific formulation outperforms the one based on RLT.
title Branch and Cut for Partitioning a Graph into a Cycle of Clusters
topic Optimization and Control
65K05, 90C11, 60-xx
url https://arxiv.org/abs/2401.08412