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Tight Bounds for Planar Strongly Connected Steiner Subgraph with Fixed Number of Terminals (and Extensions) (1911.13161v1)

Published 29 Nov 2019 in cs.DS and cs.CC

Abstract: (see paper for full abstract) Given a vertex-weighted directed graph $G=(V,E)$ and a set $T={t_1, t_2, \ldots t_k}$ of $k$ terminals, the objective of the SCSS problem is to find a vertex set $H\subseteq V$ of minimum weight such that $G[H]$ contains a $t_{i}\rightarrow t_j$ path for each $i\neq j$. The problem is NP-hard, but Feldman and Ruhl [FOCS '99; SICOMP '06] gave a novel $n{O(k)}$ algorithm for the SCSS problem, where $n$ is the number of vertices in the graph and $k$ is the number of terminals. We explore how much easier the problem becomes on planar directed graphs: - Our main algorithmic result is a $2{O(k)}\cdot n{O(\sqrt{k})}$ algorithm for planar SCSS, which is an improvement of a factor of $O(\sqrt{k})$ in the exponent over the algorithm of Feldman and Ruhl. - Our main hardness result is a matching lower bound for our algorithm: we show that planar SCSS does not have an $f(k)\cdot n{o(\sqrt{k})}$ algorithm for any computable function $f$, unless the Exponential Time Hypothesis (ETH) fails. The following additional results put our upper and lower bounds in context: - In general graphs, we cannot hope for such a dramatic improvement over the $n{O(k)}$ algorithm of Feldman and Ruhl: assuming ETH, SCSS in general graphs does not have an $f(k)\cdot n{o(k/\log k)}$ algorithm for any computable function $f$. - Feldman and Ruhl generalized their $n{O(k)}$ algorithm to the more general Directed Steiner Network (DSN) problem; here the task is to find a subgraph of minimum weight such that for every source $s_i$ there is a path to the corresponding terminal $t_i$. We show that, assuming ETH, there is no $f(k)\cdot n{o(k)}$ time algorithm for DSN on acyclic planar graphs.

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Authors (4)
  1. Rajesh Chitnis (22 papers)
  2. Andreas Emil Feldmann (27 papers)
  3. MohammadTaghi Hajiaghayi (104 papers)
  4. Dániel Marx (79 papers)
Citations (31)

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