Covering Small Independent Sets and Separators with Applications to Parameterized Algorithms

Abstract: We present two new combinatorial tools for the design of parameterized algorithms. The first is a simple linear time randomized algorithm that given as input a $d$-degenerate graph $G$ and an integer $k$, outputs an independent set $Y$, such that for every independent set $X$ in $G$ of size at most $k$, the probability that $X$ is a subset of $Y$ is at least $$\left({(d+1)k \choose k} \cdot k(d+1)\right)<sup>{-1}$$.The second is a new (deterministic) polynomial time graph sparsification procedure that given a graph $G$, a set $$T = {{s_1, t_1}, {s_2, t_2}, \ldots, {s_\ell, t_\ell}}$$ of terminal pairs and an integer $k$, returns an induced subgraph $G<sup>\star$ of $G$ that maintains all the inclusion minimal multicuts of $G$ of size at most $k$, and does not contain any $(k+2)$-vertex connected set of size $2^{{\cal O}(k)}$. In particular, $G<sup>\star$ excludes a clique of size $2^{{\cal O}(k)}$ as a topological minor. Put together, our new tools yield new randomized fixed parameter tractable (FPT) algorithms for Stable $s$-$t$ Separator, Stable Odd Cycle Transversal and Stable Multicut on general graphs, and for Stable Directed Feedback Vertex Set on $d$-degenerate graphs, resolving two problems left open by Marx et al. [ACM Transactions on Algorithms, 2013]. All of our algorithms can be derandomized at the cost of a small overhead in the running time.