Integer Programming Approaches to Balanced Connected $k$-Partition

TL;DR

This paper introduces three integer linear programming models for the NP-hard problem of partitioning a connected graph into k connected subgraphs with balanced weights, demonstrating their computational efficiency.

Contribution

The paper presents new ILP formulations for the balanced connected k-partition problem, including polyhedral insights and flow-based models, improving upon existing methods.

Findings

Proposed formulations outperform previous models in computational tests.

Polyhedral analysis provided for the unit-weight case.

Flow-based models have polynomial size and are efficient.

Abstract

We address the problem of partitioning a vertex-weighted connected graph into $k$ connected subgraphs that have similar weights, for a fixed integer $k\geq 2$. This problem, known as the \emph{balanced connected $k$-partition problem} ($BCP_k$), is defined as follows. Given a connected graph $G$ with nonnegative weights on the vertices, find a partition $\{V_i\}_{i=1}^k$ of $V(G)$ such that each class $V_i$ induces a connected subgraph of $G$, and the weight of a class with the minimum weight is as large as possible. It is known that $BCP_k$ is $NP$-hard even on bipartite graphs and on interval graphs. It has been largely investigated under different approaches and perspectives. On the practical side, $BCP_k$ is used to model many applications arising in police patrolling, image processing, cluster analysis, operating systems and robotics. We propose three integer linear programming formulations for the balanced connected $k$-partition problem. The first one contains only binary variables and a potentially large number of constraints that are separable in polynomial time. Some polyhedral results on this formulation, when all vertices have unit weight, are also presented. The other formulations are based on flows and have a polynomial number of constraints and variables. Preliminary computational experiments have shown that the proposed formulations outperform the other formulations presented in the literature.