Results 261 to 270 of about 8,227,085 (309)
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1992
Abstract We now tum to the problem of finding lower bounds for Poisson approximation. Although the Stein–Chen method is widely applicable, it would be much less interesting if it did not give accurate estimates, at least in the sense that the upper bounds it gave were of the right order of magnitude.
A D Barbour, Lars Holst, Svante Janson
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Abstract We now tum to the problem of finding lower bounds for Poisson approximation. Although the Stein–Chen method is widely applicable, it would be much less interesting if it did not give accurate estimates, at least in the sense that the upper bounds it gave were of the right order of magnitude.
A D Barbour, Lars Holst, Svante Janson
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Algebra Universalis, 2001
The authors study the hierarchy of properties that define lower bounded lattices within the class of all finite lattices. A lattice \(L\) is lower bounded if any homomorphism \(h\) from a finitely generated lattice \(K\) into \(L\) is lower bounded, i.e.\ if \(\{ x\in K\); \(a\leq h(x) \}\) is either empty or has a least element whenever \(a\in h(K)\).
Adaricheva, K. V., Gorbunov, V. A.
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The authors study the hierarchy of properties that define lower bounded lattices within the class of all finite lattices. A lattice \(L\) is lower bounded if any homomorphism \(h\) from a finitely generated lattice \(K\) into \(L\) is lower bounded, i.e.\ if \(\{ x\in K\); \(a\leq h(x) \}\) is either empty or has a least element whenever \(a\in h(K)\).
Adaricheva, K. V., Gorbunov, V. A.
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Quadratic and Near-Quadratic Lower Bounds for the CONGEST Model
International Symposium on Distributed Computing, 2017We present the first super-linear lower bounds for natural graph problems in the CONGEST model, answering a long-standing open question. Specifically, we show that any exact computation of a minimum vertex cover or a maximum independent set requires ...
K. Censor-Hillel, Seri Khoury, A. Paz
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Journal of Algorithms, 1997
Summary: We show lower bounds on the worst-case complexity of Shellsort. In particular, we give a fairly simple proof of an \(\Omega (n(\text{lg}^2n/(\text{lg lg } n)^2)\) lower bound for the size of Shellsort sorting networks for arbitrary increment sequences.
Plaxton, C. Greg, Suel, Torsten
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Summary: We show lower bounds on the worst-case complexity of Shellsort. In particular, we give a fairly simple proof of an \(\Omega (n(\text{lg}^2n/(\text{lg lg } n)^2)\) lower bound for the size of Shellsort sorting networks for arbitrary increment sequences.
Plaxton, C. Greg, Suel, Torsten
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Upper and Lower Bounds for Stochastic Processes
Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics, 2021M. Talagrand
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Lower Bounds for Kernelization
2014Kernelization is the process of transforming the input of a combinatorial decision problem to an equivalent instance, with a guarantee on the size of the resulting instances as a function of a parameter. Recent techniques from the field of fixed parameter complexity and tractability allow to give lower bounds for such kernels.
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Kernelization, Exponential Lower Bounds
2014Research on kernelization is motivated in two ways. First, when solving a hard (e.g., NP-hard) problem in practice, a common approach is to first preprocess the instance at hand before running more time-consuming methods (like integer linear programming, branch and bound, etc.). The following is a natural question.
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2013
We introduce powerful new techniques to show that FPT parameterized problems do not have polynomial-sized many : 1 kernels, under standard assumptions of classical complexity theory. A new completeness program for exploring the issue for Turing kernelization is also described.
Rodney G. Downey, Michael R. Fellows
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We introduce powerful new techniques to show that FPT parameterized problems do not have polynomial-sized many : 1 kernels, under standard assumptions of classical complexity theory. A new completeness program for exploring the issue for Turing kernelization is also described.
Rodney G. Downey, Michael R. Fellows
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Hardness magnification near state-of-the-art lower bounds
Electron. Colloquium Comput. Complex., 2019I. Oliveira, J. Pich, R. Santhanam
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