Results 21 to 30 of about 19,138 (262)
Parameterized parallel complexity [PDF]
We introduce a framework to study the parallel complexity of parameterized problems, and we propose some analogs of NC.
Cesati M., Di Ianni M.
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Counting Problems in Parameterized Complexity
Parameterized complexity is a multivariate theory for the analysis of computational problems. It leads to practically efficient algorithms for many đđ-hard problems and also provides a much finer complexity classification for other intractable problems ...
Chihao Zhang, Yijia Chen
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Parameterized complexity of firefighting
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Cristina Bazgan +5 more
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On the Parameterized Complexity of Consensus Clustering [PDF]
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Martin Dörnfelder +3 more
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On the parameterized complexity of computing tree-partitions [PDF]
We study the parameterized complexity of computing the tree-partition-width, a graph parameter equivalent to treewidth on graphs of bounded maximum degree.
Hans L. Bodlaender +2 more
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A Brief Survey of Fixed-Parameter Parallelism
This paper provides an overview of the field of parameterized parallel complexity by surveying previous work in addition to presenting a few new observations and exploring potential new directions.
Faisal N. Abu-Khzam, Karam Al Kontar
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Computation Models for Parameterized Complexity [PDF]
AbstractA parameterized computational problem is a set of pairs (x,k), wherekis a distinguished item called âparameterâ. FPT is the class of fixedâparameter tractable problems: for any fixed value ofk, they are solvable in time bounded by a polynomial of degree α, where α is a constant not dependent on the parameter. In order to deal with parameterized
Cesati M., Di Ianni M.
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Parameterized Complexity of Geodetic Set
A vertex set $S$ of a graph $G$ is geodetic if every vertex of $G$ lies on a shortest path between two vertices in $S$. Given a graph $G$ and $k \in \mathbb{N}$, the NP-hard ${\rm G{\small EODETIC}~S{ \small ET}}$ problem asks whether there is a geodetic set of size at most $k$.
Leon Kellerhals, Tomohiro Koana
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A Compendium of Parameterized Problems at Higher Levels of the Polynomial Hierarchy
We present a list of parameterized problems together with a complexity classification of whether they allow a fixed-parameter tractable reduction to SAT or not.
Ronald de Haan, Stefan Szeider
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Taming the Chaos in Neural Network Time Series Predictions
Machine learning methods, such as Long Short-Term Memory (LSTM) neural networks can predict real-life time series data. Here, we present a new approach to predict time series data combining interpolation techniques, randomly parameterized LSTM neural ...
Sebastian Raubitzek, Thomas Neubauer
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