DMLGOCFeb 16, 2022

A Polyhedral Study of Lifted Multicuts

arXiv:2202.08068v15 citations
AI Analysis

This work provides a theoretical foundation for graph decomposition methods in data analysis, but it is incremental as it builds on prior lifting concepts.

The authors studied the polytope associated with lifted multicuts in graphs, connecting it to existing work on clique partitioning and multilinear polytopes to enhance the characterization of graph decompositions.

Fundamental to many applications in data analysis are the decompositions of a graph, i.e. partitions of the node set into component-inducing subsets. One way of encoding decompositions is by multicuts, the subsets of those edges that straddle distinct components. Recently, a lifting of multicuts from a graph $G = (V, E)$ to an augmented graph $\hat G = (V, E \cup F)$ has been proposed in the field of image analysis, with the goal of obtaining a more expressive characterization of graph decompositions in which it is made explicit also for pairs $F \subseteq \tbinom{V}{2} \setminus E$ of non-neighboring nodes whether these are in the same or distinct components. In this work, we study in detail the polytope in $\mathbb{R}^{E \cup F}$ whose vertices are precisely the characteristic vectors of multicuts of $\hat G$ lifted from $G$, connecting it, in particular, to the rich body of prior work on the clique partitioning and multilinear polytope.

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