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On the Parameterized Complexity of Reconfiguration Problems

We present the first results on the parameterized complexity of reconfiguration problems, where a reconfiguration version of an optimization problem $Q$ takes as input two feasible solutions $S$ and $T$ and determines if there is a sequence of {\em reconfiguration steps} that can be applied to transform $S$ into $T$ such that each step results in a feasible solution to $Q$. For most of the results in this paper, $S$ and $T$ are subsets of vertices of a given graph and a reconfiguration step adds or deletes a vertex. Our study is motivated by recent results establishing that for most NP-hard problems, the classical complexity of reconfiguration is PSPACE-complete. We address the question for several important graph properties under two natural parameterizations: $k$, the size of the solutions, and $\ell$, the length of the sequence of steps. Our first general result is an algorithmic paradigm, the {\em reconfiguration kernel}, used to obtain fixed-parameter algorithms for the reconfiguration versions of {\sc Vertex Cover} and, more generally, {\sc Bounded Hitting Set} and {\sc Feedback Vertex Set}, all parameterized by $k$. In contrast, we show that reconfiguring {\sc Unbounded Hitting Set} is $W[2]$-hard when parameterized by $k+\ell$. We also demonstrate the $W[1]$-hardness of the reconfiguration versions of a large class of maximization problems parameterized by $k+\ell$, and of their corresponding deletion problems parameterized by $\ell$; in doing so, we show that there exist problems in FPT when parameterized by $k$, but whose reconfiguration versions are $W[1]$-hard when parameterized by $k+\ell$.