Abstract

An input to a conflict-free variant of a classical problem $$\Gamma $$, called Conflict-Free$$\Gamma $$, consists of an instance I of $$\Gamma $$ coupled with a graph H, called the conflict graph. A solution to Conflict-Free$$\Gamma $$ in (I, H) is a solution to I in $$\Gamma $$, which is also an independent set in H. In this paper, we study conflict-free variants of Maximum Matching and Shortest Path, which we call Conflict-Free Maximum Matching (CF-MM) and Conflict-Free Shortest Path (CF-SP), respectively. We show that both CF-MM and CF-SP are W[1]-hard, when parameterized by the solution size. Moreover, W[1]-hardness for CF-MM holds even when the input graph where we want to find a matching is itself a matching, and W[1]-hardness for CF-SP holds for conflict graph being a unit-interval graph. Next, we study these problems with restriction on the conflict graphs. We give FPT algorithms for CF-MM when the conflict graph is chordal. Also, we give FPT algorithms for both CF-MM and CF-SP, when the conflict graph is d-degenerate. Finally, we design FPT algorithms for variants of CF-MM and CF-SP, where the conflicting conditions are given by a (representable) matroid.

Highlights

  • In the recent years, conflict-free variant of classical combinatorial optimization problems have gained attention from the viewpoint of algorithmic complexity

  • We study conflict-free variants of Maximum Matching and Shortest Path, which we call Conflict Free Maximum Matching (CF-MM, for short) and Conflict Free Shortest Path (CF-SP, for short), respectively

  • For the CF-MM problem, we give an FPT algorithm, when the conflict graph belongs to the family of chordal graphs

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Summary

Introduction

Conflict-free variant of classical combinatorial optimization problems have gained attention from the viewpoint of algorithmic complexity. A solution to Conflict-Free Γ in (I, H) is a solution to I in Γ, which is an independent set in H. Notice that conflict-free version of the problem introduces the constraint of “impossible pairs” in the solution that we seek for. Such a constraint of “impossible pairs” in a solution arises, for example, in the context of program testing and validation [16, 23]. Gabow et al [16] studied the conflict-free version of paths in a graph, which they showed to be NP-complete

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