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Titlebook: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques; 12th International W Irit Dinur,Klaus Jansen,José

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51#
發(fā)表于 2025-3-30 09:59:05 | 只看該作者
Smooth Dynamical Systems on Smooth Manifoldsed version of Diophantine approximation is also hard to approximate. Furthermore we prove that the . problem with arbitrary capacities is NP-hard. This solves an open problem raised by Conforti, Di?Summa and Wolsey.
52#
發(fā)表于 2025-3-30 13:25:31 | 只看該作者
I. P. Cornfeld,S. V. Fomin,Ya. G. Sinairoblems is not optimal in our framework. We design a new LP relaxation and show that this LP relaxation coupled with a new randomized rounding technique is optimal in our framework..In passing, we note that our results strictly improve over previous results of Kleinberg, Papadimitriou and Raghavan [
53#
發(fā)表于 2025-3-30 19:29:30 | 只看該作者
Ergodic Theory and Dynamical Systemsn integrality gap of 4, even in this special case. Then we prove that the problem is NP-hard to approximate within a factor of 2 assuming the Unique Games Conjecture; and it is unconditionally NP-hard to approximate within a factor 17/16. Finally, we extend the APX-hardness of the problem to the spe
54#
發(fā)表于 2025-3-30 20:41:53 | 只看該作者
Ergodic Theory and Dynamical Systemsal., FOCS 2006] and [Chekuri et al., SODA 2007]. This technique seems quite robust and was already used in order to improve the ratio of Buy-at-bulk with protection (Antonakopoulos et al FOCS 2007) from log.. to log... See ?..We also consider the . (.) problem which is closely related to .: given a
55#
發(fā)表于 2025-3-31 04:21:35 | 只看該作者
56#
發(fā)表于 2025-3-31 07:27:38 | 只看該作者
57#
發(fā)表于 2025-3-31 10:43:55 | 只看該作者
58#
發(fā)表于 2025-3-31 14:24:38 | 只看該作者
New Hardness Results for Diophantine Approximationed version of Diophantine approximation is also hard to approximate. Furthermore we prove that the . problem with arbitrary capacities is NP-hard. This solves an open problem raised by Conforti, Di?Summa and Wolsey.
59#
發(fā)表于 2025-3-31 18:48:09 | 只看該作者
PASS Approximationroblems is not optimal in our framework. We design a new LP relaxation and show that this LP relaxation coupled with a new randomized rounding technique is optimal in our framework..In passing, we note that our results strictly improve over previous results of Kleinberg, Papadimitriou and Raghavan [
60#
發(fā)表于 2025-4-1 01:29:19 | 只看該作者
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