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Using contracted solution graphs for solving reconfiguration problems

Authors: Paul Bonsma; Daniël Paulusma;

Using contracted solution graphs for solving reconfiguration problems

Abstract

We introduce in a general setting a dynamic programming method for solving reconfiguration problems. Our method is based on contracted solution graphs, which are obtained from solution graphs by performing an appropriate series of edge contractions that decrease the graph size without losing any critical information needed to solve the reconfiguration problem under consideration. Our general framework captures the approach behind known reconfiguration results of Bonsma (2012) and Hatanaka, Ito and Zhou (2014). As a third example, we apply the method to the following problem: given two $k$-colorings $��$ and $��$ of a graph $G$, can $��$ be modified into $��$ by recoloring one vertex of $G$ at a time, while maintaining a $k$-coloring throughout? This problem is known to be PSPACE-hard even for bipartite planar graphs and $k=4$. By applying our method in combination with a thorough exploitation of the graph structure we obtain a polynomial time algorithm for $(k-2)$-connected chordal graphs.

Keywords

dynamic programming, FOS: Computer and information sciences, reconfiguration, 511, contraction, Programming involving graphs or networks, Computational Complexity (cs.CC), 004, Computer Science - Computational Complexity, Coloring of graphs and hypergraphs, Graph algorithms (graph-theoretic aspects), Computer Science - Data Structures and Algorithms, graph coloring, Analysis of algorithms, Data Structures and Algorithms (cs.DS), ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
Green
hybrid