15,354 research outputs found
Hierarchies of Inefficient Kernelizability
The framework of Bodlaender et al. (ICALP 2008) and Fortnow and Santhanam
(STOC 2008) allows us to exclude the existence of polynomial kernels for a
range of problems under reasonable complexity-theoretical assumptions. However,
there are also some issues that are not addressed by this framework, including
the existence of Turing kernels such as the "kernelization" of Leaf Out
Branching(k) into a disjunction over n instances of size poly(k). Observing
that Turing kernels are preserved by polynomial parametric transformations, we
define a kernelization hardness hierarchy, akin to the M- and W-hierarchy of
ordinary parameterized complexity, by the PPT-closure of problems that seem
likely to be fundamentally hard for efficient Turing kernelization. We find
that several previously considered problems are complete for our fundamental
hardness class, including Min Ones d-SAT(k), Binary NDTM Halting(k), Connected
Vertex Cover(k), and Clique(k log n), the clique problem parameterized by k log
n
Exact Methods for Multistage Estimation of a Binomial Proportion
We first review existing sequential methods for estimating a binomial
proportion. Afterward, we propose a new family of group sequential sampling
schemes for estimating a binomial proportion with prescribed margin of error
and confidence level. In particular, we establish the uniform controllability
of coverage probability and the asymptotic optimality for such a family of
sampling schemes. Our theoretical results establish the possibility that the
parameters of this family of sampling schemes can be determined so that the
prescribed level of confidence is guaranteed with little waste of samples.
Analytic bounds for the cumulative distribution functions and expectations of
sample numbers are derived. Moreover, we discuss the inherent connection of
various sampling schemes. Numerical issues are addressed for improving the
accuracy and efficiency of computation. Computational experiments are conducted
for comparing sampling schemes. Illustrative examples are given for
applications in clinical trials.Comment: 38 pages, 9 figure
A practical fpt algorithm for Flow Decomposition and transcript assembly
The Flow Decomposition problem, which asks for the smallest set of weighted
paths that "covers" a flow on a DAG, has recently been used as an important
computational step in transcript assembly. We prove the problem is in FPT when
parameterized by the number of paths by giving a practical linear fpt
algorithm. Further, we implement and engineer a Flow Decomposition solver based
on this algorithm, and evaluate its performance on RNA-sequence data.
Crucially, our solver finds exact solutions while achieving runtimes
competitive with a state-of-the-art heuristic. Finally, we contextualize our
design choices with two hardness results related to preprocessing and weight
recovery. Specifically, -Flow Decomposition does not admit polynomial
kernels under standard complexity assumptions, and the related problem of
assigning (known) weights to a given set of paths is NP-hard.Comment: Introduces software package Toboggan: Version 1.0.
http://dx.doi.org/10.5281/zenodo.82163
Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges
Computational Social Choice is an interdisciplinary research area involving
Economics, Political Science, and Social Science on the one side, and
Mathematics and Computer Science (including Artificial Intelligence and
Multiagent Systems) on the other side. Typical computational problems studied
in this field include the vulnerability of voting procedures against attacks,
or preference aggregation in multi-agent systems. Parameterized Algorithmics is
a subfield of Theoretical Computer Science seeking to exploit meaningful
problem-specific parameters in order to identify tractable special cases of in
general computationally hard problems. In this paper, we propose nine of our
favorite research challenges concerning the parameterized complexity of
problems appearing in this context
Counting Answers to Existential Positive Queries: A Complexity Classification
Existential positive formulas form a fragment of first-order logic that
includes and is semantically equivalent to unions of conjunctive queries, one
of the most important and well-studied classes of queries in database theory.
We consider the complexity of counting the number of answers to existential
positive formulas on finite structures and give a trichotomy theorem on query
classes, in the setting of bounded arity. This theorem generalizes and unifies
several known results on the complexity of conjunctive queries and unions of
conjunctive queries.Comment: arXiv admin note: substantial text overlap with arXiv:1501.0719
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