## Abstract

Given a graph G=(V,E), a coloring function χ:V→C, assigning each vertex a color, is called convex if, for every color c∈C, the set of vertices with color c induces a connected subgraph of G. In the CONVEX RECOLORING problem a colored graph G_{χ} is given, and the goal is to find a convex coloring χ^{′} of G that recolors a minimum number of vertices. In the weighted version each vertex has a weight, and the goal is to minimize the total weight of recolored vertices. The 2-CONVEX RECOLORING problem (2-CR) is the special case, where the given coloring χ assigns the same color to at most two vertices. 2-CR is known to be NP-hard even if G is a path. We show that weighted 2-CR cannot be approximated within any ratio, unless P=NP. On the other hand, we provide an alternative definition of (unweighted) 2-CR in terms of maximum independent set of paths, which leads to a natural greedy algorithm. We prove that its approximation ratio is [Formula presented] and show that this analysis is tight. This is the first constant factor approximation algorithm for a variant of CR in general graphs. For the special case, where G is a path, the algorithm obtains a ratio of [Formula presented], an improvement over the previous best known approximation. We also consider the problem of determining whether a given graph has a convex recoloring of size k. We use the above mentioned characterization of 2-CR to show that a problem kernel of size 4k can be obtained in linear time and to design an O(|E|)+2^{O(klogk)} time algorithm for parameterized 2-CR.

Original language | English |
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Pages (from-to) | 2-11 |

Number of pages | 10 |

Journal | Discrete Applied Mathematics |

Volume | 246 |

DOIs | |

State | Published - 10 Sep 2018 |

### Bibliographical note

Publisher Copyright:© 2017 Elsevier B.V.

### Funding

The third author was supported in part by the Israel Science Foundation (Grant No. 497/14 ).

Funders | Funder number |
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Israel Science Foundation | 497/14 |

## Keywords

- Approximation algorithms
- Convex recoloring
- Greedy