Papers
Topics
Authors
Recent
Search
2000 character limit reached

Conditionally optimal approximation algorithms for the girth of a directed graph

Published 23 Apr 2020 in cs.DS | (2004.11445v2)

Abstract: It is known that a better than $2$-approximation algorithm for the girth in dense directed unweighted graphs needs $n{3-o(1)}$ time unless one uses fast matrix multiplication. Meanwhile, the best known approximation factor for a combinatorial algorithm running in $O(mn{1-\epsilon})$ time (by Chechik et al.) is $3$. Is the true answer $2$ or $3$? The main result of this paper is a (conditionally) tight approximation algorithm for directed graphs. First, we show that under a popular hardness assumption, any algorithm, even one that exploits fast matrix multiplication, would need to take at least $mn{1-o(1)}$ time for some sparsity $m$ if it achieves a $(2-\epsilon)$-approximation for any $\epsilon>0$. Second we give a $2$-approximation algorithm for the girth of unweighted graphs running in $\tilde{O}(mn{3/4})$ time, and a $(2+\epsilon)$-approximation algorithm (for any $\epsilon>0$) that works in weighted graphs and runs in $\tilde{O}(m\sqrt n)$ time. Our algorithms are combinatorial. We also obtain a $(4+\epsilon)$-approximation of the girth running in $\tilde{O}(mn{\sqrt{2}-1})$ time, improving upon the previous best $\tilde{O}(m\sqrt n)$ running time by Chechik et al. Finally, we consider the computation of roundtrip spanners. We obtain a $(5+\epsilon)$-approximate roundtrip spanner on $\tilde{O}(n{1.5}/\epsilon2)$ edges in $\tilde{O}(m\sqrt n/\epsilon2)$ time. This improves upon the previous approximation factor $(8+\epsilon)$ of Chechik et al. for the same running time.

Citations (8)

Summary

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.