122 research outputs found
Minimal Suffix and Rotation of a Substring in Optimal Time
For a text given in advance, the substring minimal suffix queries ask to
determine the lexicographically minimal non-empty suffix of a substring
specified by the location of its occurrence in the text. We develop a data
structure answering such queries optimally: in constant time after linear-time
preprocessing. This improves upon the results of Babenko et al. (CPM 2014),
whose trade-off solution is characterized by product of these
time complexities. Next, we extend our queries to support concatenations of
substrings, for which the construction and query time is preserved. We
apply these generalized queries to compute lexicographically minimal and
maximal rotations of a given substring in constant time after linear-time
preprocessing.
Our data structures mainly rely on properties of Lyndon words and Lyndon
factorizations. We combine them with further algorithmic and combinatorial
tools, such as fusion trees and the notion of order isomorphism of strings
Constant Factor Approximation for Capacitated k-Center with Outliers
The -center problem is a classic facility location problem, where given an
edge-weighted graph one is to find a subset of vertices ,
such that each vertex in is "close" to some vertex in . The
approximation status of this basic problem is well understood, as a simple
2-approximation algorithm is known to be tight. Consequently different
extensions were studied.
In the capacitated version of the problem each vertex is assigned a capacity,
which is a strict upper bound on the number of clients a facility can serve,
when located at this vertex. A constant factor approximation for the
capacitated -center was obtained last year by Cygan, Hajiaghayi and Khuller
[FOCS'12], which was recently improved to a 9-approximation by An, Bhaskara and
Svensson [arXiv'13].
In a different generalization of the problem some clients (denoted as
outliers) may be disregarded. Here we are additionally given an integer and
the goal is to serve exactly clients, which the algorithm is free to
choose. In 2001 Charikar et al. [SODA'01] presented a 3-approximation for the
-center problem with outliers.
In this paper we consider a common generalization of the two extensions
previously studied separately, i.e. we work with the capacitated -center
with outliers. We present the first constant factor approximation algorithm
with approximation ratio of 25 even for the case of non-uniform hard
capacities.Comment: 15 pages, 3 figures, accepted to STACS 201
Approximating Upper Degree-Constrained Partial Orientations
In the Upper Degree-Constrained Partial Orientation problem we are given an
undirected graph , together with two degree constraint functions
. The goal is to orient as many edges as possible,
in such a way that for each vertex the number of arcs entering is
at most , whereas the number of arcs leaving is at most .
This problem was introduced by Gabow [SODA'06], who proved it to be MAXSNP-hard
(and thus APX-hard). In the same paper Gabow presented an LP-based iterative
rounding -approximation algorithm.
Since the problem in question is a special case of the classic 3-Dimensional
Matching, which in turn is a special case of the -Set Packing problem, it is
reasonable to ask whether recent improvements in approximation algorithms for
the latter two problems [Cygan, FOCS'13; Sviridenko & Ward, ICALP'13] allow for
an improved approximation for Upper Degree-Constrained Partial Orientation. We
follow this line of reasoning and present a polynomial-time local search
algorithm with approximation ratio . Our algorithm uses a
combination of two types of rules: improving sets of bounded pathwidth from the
recent -approximation algorithm for 3-Set Packing [Cygan,
FOCS'13], and a simple rule tailor-made for the setting of partial
orientations. In particular, we exploit the fact that one can check in
polynomial time whether it is possible to orient all the edges of a given graph
[Gy\'arf\'as & Frank, Combinatorics'76].Comment: 12 pages, 1 figur
Faster Longest Common Extension Queries in Strings over General Alphabets
Longest common extension queries (often called longest common prefix queries)
constitute a fundamental building block in multiple string algorithms, for
example computing runs and approximate pattern matching. We show that a
sequence of LCE queries for a string of size over a general ordered
alphabet can be realized in time making only
symbol comparisons. Consequently, all runs in a string over a general
ordered alphabet can be computed in time making
symbol comparisons. Our results improve upon a solution by Kosolobov
(Information Processing Letters, 2016), who gave an algorithm with running time and conjectured that time is possible. We
make a significant progress towards resolving this conjecture. Our techniques
extend to the case of general unordered alphabets, when the time increases to
. The main tools are difference covers and the
disjoint-sets data structure.Comment: Accepted to CPM 201
Internal Pattern Matching Queries in a Text and Applications
We consider several types of internal queries: questions about subwords of a
text. As the main tool we develop an optimal data structure for the problem
called here internal pattern matching. This data structure provides
constant-time answers to queries about occurrences of one subword in
another subword of a given text, assuming that ,
which allows for a constant-space representation of all occurrences. This
problem can be viewed as a natural extension of the well-studied pattern
matching problem. The data structure has linear size and admits a linear-time
construction algorithm.
Using the solution to the internal pattern matching problem, we obtain very
efficient data structures answering queries about: primitivity of subwords,
periods of subwords, general substring compression, and cyclic equivalence of
two subwords. All these results improve upon the best previously known
counterparts. The linear construction time of our data structure also allows to
improve the algorithm for finding -subrepetitions in a text (a more
general version of maximal repetitions, also called runs). For any fixed
we obtain the first linear-time algorithm, which matches the linear
time complexity of the algorithm computing runs. Our data structure has already
been used as a part of the efficient solutions for subword suffix rank &
selection, as well as substring compression using Burrows-Wheeler transform
composed with run-length encoding.Comment: 31 pages, 9 figures; accepted to SODA 201
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