Results 271 to 280 of about 13,257,834 (322)
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On lower bounded lattices

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.
openaire   +1 more source

Lower Bounds for Shellsort

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
openaire   +2 more sources

A New Lower Bound for Classic Online Bin Packing

Algorithmica, 2018
We improve the lower bound on the asymptotic competitive ratio of any online algorithm for bin packing to above 1.54278. We demonstrate for the first time the advantage of branching and the applicability of full adaptivity in the design of lower bounds ...
J. Balogh   +4 more
semanticscholar   +1 more source

Stability and stabilization of a class of stochastic switching systems with lower bound of sojourn time

at - Automatisierungstechnik, 2018
This paper is concerned with the stability and stabilization issues for a family of discrete-time stochastic switching systems with bounded sojourn time.
Zepeng Ning, Lixian Zhang, J. Lam
semanticscholar   +1 more source

Eldan's Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion

IEEE Annual Symposium on Foundations of Computer Science, 2016
We show that the KLS constant for n-dimensional isotropic logconcavemeasures is O(n^{1/4}), improving on the current best bound ofO(n^{1/3}√{\log n}).
Y. Lee, S. Vempala
semanticscholar   +1 more source

Lower Bounds for Kernelization

2014
Kernelization 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.
openaire   +1 more source

Kernelization, Exponential Lower Bounds

2014
Research 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.
openaire   +1 more source

Kernelization Lower Bounds

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
openaire   +1 more source

Yes, There is an Oblivious RAM Lower Bound!

IACR Cryptology ePrint Archive, 2018
Kasper Green Larsen, J. Nielsen
semanticscholar   +1 more source

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