1,435 research outputs found
Algorithmic Applications of Baur-Strassen's Theorem: Shortest Cycles, Diameter and Matchings
Consider a directed or an undirected graph with integral edge weights from
the set [-W, W], that does not contain negative weight cycles. In this paper,
we introduce a general framework for solving problems on such graphs using
matrix multiplication. The framework is based on the usage of Baur-Strassen's
theorem and of Strojohann's determinant algorithm. It allows us to give new and
simple solutions to the following problems:
* Finding Shortest Cycles -- We give a simple \tilde{O}(Wn^{\omega}) time
algorithm for finding shortest cycles in undirected and directed graphs. For
directed graphs (and undirected graphs with non-negative weights) this matches
the time bounds obtained in 2011 by Roditty and Vassilevska-Williams. On the
other hand, no algorithm working in \tilde{O}(Wn^{\omega}) time was previously
known for undirected graphs with negative weights. Furthermore our algorithm
for a given directed or undirected graph detects whether it contains a negative
weight cycle within the same running time.
* Computing Diameter and Radius -- We give a simple \tilde{O}(Wn^{\omega})
time algorithm for computing a diameter and radius of an undirected or directed
graphs. To the best of our knowledge no algorithm with this running time was
known for undirected graphs with negative weights.
* Finding Minimum Weight Perfect Matchings -- We present an
\tilde{O}(Wn^{\omega}) time algorithm for finding minimum weight perfect
matchings in undirected graphs. This resolves an open problem posted by
Sankowski in 2006, who presented such an algorithm but only in the case of
bipartite graphs.
In order to solve minimum weight perfect matching problem we develop a novel
combinatorial interpretation of the dual solution which sheds new light on this
problem. Such a combinatorial interpretation was not know previously, and is of
independent interest.Comment: To appear in FOCS 201
An Even Faster and More Unifying Algorithm for Comparing Trees via Unbalanced Bipartite Matchings
A widely used method for determining the similarity of two labeled trees is
to compute a maximum agreement subtree of the two trees. Previous work on this
similarity measure is only concerned with the comparison of labeled trees of
two special kinds, namely, uniformly labeled trees (i.e., trees with all their
nodes labeled by the same symbol) and evolutionary trees (i.e., leaf-labeled
trees with distinct symbols for distinct leaves). This paper presents an
algorithm for comparing trees that are labeled in an arbitrary manner. In
addition to this generality, this algorithm is faster than the previous
algorithms.
Another contribution of this paper is on maximum weight bipartite matchings.
We show how to speed up the best known matching algorithms when the input
graphs are node-unbalanced or weight-unbalanced. Based on these enhancements,
we obtain an efficient algorithm for a new matching problem called the
hierarchical bipartite matching problem, which is at the core of our maximum
agreement subtree algorithm.Comment: To appear in Journal of Algorithm
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