126 research outputs found
Parameterizing by the Number of Numbers
The usefulness of parameterized algorithmics has often depended on what
Niedermeier has called, "the art of problem parameterization". In this paper we
introduce and explore a novel but general form of parameterization: the number
of numbers. Several classic numerical problems, such as Subset Sum, Partition,
3-Partition, Numerical 3-Dimensional Matching, and Numerical Matching with
Target Sums, have multisets of integers as input. We initiate the study of
parameterizing these problems by the number of distinct integers in the input.
We rely on an FPT result for ILPF to show that all the above-mentioned problems
are fixed-parameter tractable when parameterized in this way. In various
applied settings, problem inputs often consist in part of multisets of integers
or multisets of weighted objects (such as edges in a graph, or jobs to be
scheduled). Such number-of-numbers parameterized problems often reduce to
subproblems about transition systems of various kinds, parameterized by the
size of the system description. We consider several core problems of this kind
relevant to number-of-numbers parameterization. Our main hardness result
considers the problem: given a non-deterministic Mealy machine M (a finite
state automaton outputting a letter on each transition), an input word x, and a
census requirement c for the output word specifying how many times each letter
of the output alphabet should be written, decide whether there exists a
computation of M reading x that outputs a word y that meets the requirement c.
We show that this problem is hard for W[1]. If the question is whether there
exists an input word x such that a computation of M on x outputs a word that
meets c, the problem becomes fixed-parameter tractable
Cluster Editing: Kernelization based on Edge Cuts
Kernelization algorithms for the {\sc cluster editing} problem have been a
popular topic in the recent research in parameterized computation. Thus far
most kernelization algorithms for this problem are based on the concept of {\it
critical cliques}. In this paper, we present new observations and new
techniques for the study of kernelization algorithms for the {\sc cluster
editing} problem. Our techniques are based on the study of the relationship
between {\sc cluster editing} and graph edge-cuts. As an application, we
present an -time algorithm that constructs a kernel for the
{\it weighted} version of the {\sc cluster editing} problem. Our result meets
the best kernel size for the unweighted version for the {\sc cluster editing}
problem, and significantly improves the previous best kernel of quadratic size
for the weighted version of the problem
Polynomial kernels for 3-leaf power graph modification problems
A graph G=(V,E) is a 3-leaf power iff there exists a tree T whose leaves are
V and such that (u,v) is an edge iff u and v are at distance at most 3 in T.
The 3-leaf power graph edge modification problems, i.e. edition (also known as
the closest 3-leaf power), completion and edge-deletion, are FTP when
parameterized by the size of the edge set modification. However polynomial
kernel was known for none of these three problems. For each of them, we provide
cubic kernels that can be computed in linear time for each of these problems.
We thereby answer an open problem first mentioned by Dom, Guo, Huffner and
Niedermeier (2005).Comment: Submitte
Reconstructing phylogenies from noisy quartets in polynomial time with a high success probability
<p>Abstract</p> <p>Background</p> <p>In recent years, quartet-based phylogeny reconstruction methods have received considerable attentions in the computational biology community. Traditionally, the accuracy of a phylogeny reconstruction method is measured by simulations on synthetic datasets with known "true" phylogenies, while little theoretical analysis has been done. In this paper, we present a new model-based approach to measuring the accuracy of a quartet-based phylogeny reconstruction method. Under this model, we propose three efficient algorithms to reconstruct the "true" phylogeny with a high success probability.</p> <p>Results</p> <p>The first algorithm can reconstruct the "true" phylogeny from the input quartet topology set without quartet errors in <it>O</it>(<it>n</it><sup>2</sup>) time by querying at most (<it>n </it>- 4) log(<it>n </it>- 1) quartet topologies, where <it>n </it>is the number of the taxa. When the input quartet topology set contains errors, the second algorithm can reconstruct the "true" phylogeny with a probability approximately 1 - <it>p </it>in <it>O</it>(<it>n</it><sup>4 </sup>log <it>n</it>) time, where <it>p </it>is the probability for a quartet topology being an error. This probability is improved by the third algorithm to approximately <inline-formula><m:math name="1748-7188-3-1-i1" xmlns:m="http://www.w3.org/1998/Math/MathML"><m:semantics><m:mrow><m:mfrac><m:mn>1</m:mn><m:mrow><m:mn>1</m:mn><m:mo>+</m:mo><m:msup><m:mi>q</m:mi><m:mn>2</m:mn></m:msup><m:mo>+</m:mo><m:mfrac><m:mn>1</m:mn><m:mn>2</m:mn></m:mfrac><m:msup><m:mi>q</m:mi><m:mn>4</m:mn></m:msup><m:mo>+</m:mo><m:mfrac><m:mn>1</m:mn><m:mrow><m:mn>16</m:mn></m:mrow></m:mfrac><m:msup><m:mi>q</m:mi><m:mn>5</m:mn></m:msup></m:mrow></m:mfrac></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaqcfa4aaSaaaeaacqaIXaqmaeaacqaIXaqmcqGHRaWkcqWGXbqCdaahaaqabeaacqaIYaGmaaGaey4kaSYaaSaaaeaacqaIXaqmaeaacqaIYaGmaaGaemyCae3aaWbaaeqabaGaeGinaqdaaiabgUcaRmaalaaabaGaeGymaedabaGaeGymaeJaeGOnaydaaiabdghaXnaaCaaabeqaaiabiwda1aaaaaaaaa@3D5A@</m:annotation></m:semantics></m:math></inline-formula>, where <inline-formula><m:math name="1748-7188-3-1-i2" xmlns:m="http://www.w3.org/1998/Math/MathML"><m:semantics><m:mrow><m:mi>q</m:mi><m:mo>=</m:mo><m:mfrac><m:mi>p</m:mi><m:mrow><m:mn>1</m:mn><m:mo>−</m:mo><m:mi>p</m:mi></m:mrow></m:mfrac></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaGaemyCaeNaeyypa0tcfa4aaSaaaeaacqWGWbaCaeaacqaIXaqmcqGHsislcqWGWbaCaaaaaa@3391@</m:annotation></m:semantics></m:math></inline-formula>, with running time of <it>O</it>(<it>n</it><sup>5</sup>), which is at least 0.984 when <it>p </it>< 0.05.</p> <p>Conclusion</p> <p>The three proposed algorithms are mathematically guaranteed to reconstruct the "true" phylogeny with a high success probability. The experimental results showed that the third algorithm produced phylogenies with a higher probability than its aforementioned theoretical lower bound and outperformed some existing phylogeny reconstruction methods in both speed and accuracy.</p
Psychometric evaluation of the Sinhalese version of MacNew Heart Disease Health Related Quality of Life Questionnaire in patients with stable angina
The muscle scar in cavellinids and its importance for the phylogeny of platycope ostracodes
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