Results 21 to 30 of about 150,456 (276)
Parameterized learning complexity [PDF]
Proceedings of the sixth annual conference on Computational learning theory - COLT '93, 1993 We describe three applications in computational learning theory of techniques and ideas recently introduced in the study of parameterized computational complexity. (1) Using parameterized problem reducibilities, we show that P -sized DNF (CNF) formulas can be exactly learned in time polynomial in the number of variables by extended equivalence queries ...
Rodney G. Downey +2 more
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Quantum Parameterized Complexity
, 2022 23 pages, 1 ...
Bremner, Michael J. +5 more
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Is FFT Fast Enough for Beyond 5G Communications? A Throughput-Complexity Analysis for OFDM Signals
IEEE Access, 2022 In this paper, we study the impact of computational complexity on the throughput limits of the fast Fourier transform (FFT) algorithm for orthogonal frequency division multiplexing (OFDM) waveforms.
Saulo Queiroz +2 more
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Parameterized Complexity of Geodetic Set
Journal of Graph Algorithms and Applications, 2022 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 $k$.
Kellerhals, Leon, Koana, Tomohiro
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Parameterized Complexity of Critical Node Cuts [PDF]
, 2015 We consider the following natural graph cut problem called Critical Node Cut (CNC): Given a graph $G$ on $n$ vertices, and two positive integers $k$ and $x$, determine whether $G$ has a set of $k$ vertices whose removal leaves $G$ with at most $x ...
Hermelin, Danny +3 more
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A Brief Survey of Fixed-Parameter Parallelism
Algorithms, 2020 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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Taming the Chaos in Neural Network Time Series Predictions
Entropy, 2021 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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Parameterized Complexity of Secluded Connectivity Problems [PDF]
, 2015 The Secluded Path problem models a situation where a sensitive information has to be transmitted between a pair of nodes along a path in a network. The measure of the quality of a selected path is its exposure, which is the total weight of vertices in ...
Fomin, Fedor V. +3 more
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Computation Models for Parameterized Complexity [PDF]
Mathematical Logic Quarterly, 1997 AbstractA parameterized computational problem is a set of pairs (x,k), wherekis a distinguished item called “parameter”. FPT is the class of fixed‐parameter 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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On the parameterized complexity of computing tree-partitions [PDF]
Discrete Mathematics & Theoretical Computer ScienceWe 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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