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Titlebook: Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques; 7th International Wo Klaus Jansen,Sanjeev Khanna,Da

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樓主: Clinton
11#
發(fā)表于 2025-3-23 12:30:46 | 只看該作者
Ergodic Theory and Related Topics IIIproblem; we feel that this idea may have wider implications. Our algorithm is conceptually simpler than the previous such cost-sharing method due to Pál and Tardos (., pages 584–593, 2003), and has a much improved approximation factor of 4.6 (over the previously known factor of?15).
12#
發(fā)表于 2025-3-23 17:22:34 | 只看該作者
13#
發(fā)表于 2025-3-23 21:27:53 | 只看該作者
14#
發(fā)表于 2025-3-23 22:29:31 | 只看該作者
15#
發(fā)表于 2025-3-24 03:05:21 | 只看該作者
16#
發(fā)表于 2025-3-24 09:20:31 | 只看該作者
https://doi.org/10.1007/978-3-319-74908-23]. We give an .(log.) approximation for this problem by using Garg?.’s?[10] algorithm for the multi-cut problem as a subroutine. Our algorithm also gives an .(log..) approximation for the .. norm of the additive distortion.
17#
發(fā)表于 2025-3-24 11:11:33 | 只看該作者
18#
發(fā)表于 2025-3-24 18:30:10 | 只看該作者
The Greedy Algorithm for the Minimum Common String Partition Problemut string, is NP-hard. We study a greedy algorithm for . that at each step extracts a longest common substring from the given strings. We show that the approximation ratio of this algorithm is between Ω(..) and .(..). In case of 2-., we show that the approximation ratio is equal to 3. For 4-., we give a lower bound of Ω(log .).
19#
發(fā)表于 2025-3-24 21:44:47 | 只看該作者
Approximating Additive Distortion of Embeddings into Line Metrics3]. We give an .(log.) approximation for this problem by using Garg?.’s?[10] algorithm for the multi-cut problem as a subroutine. Our algorithm also gives an .(log..) approximation for the .. norm of the additive distortion.
20#
發(fā)表于 2025-3-25 01:50:56 | 只看該作者
Peculiar submeasures on finite algebras, question by giving an algorithm for separable, differentiable, concave utility functions satisfying gross substitutes. Our algorithm uses the auction based approach of [10]..We also outline an extension of our method to the Walrasian model.
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