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Titlebook: Approximation and Online Algorithms; 14th International W Klaus Jansen,Monaldo Mastrolilli Conference proceedings 2017 Springer Internation

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楼主: 断头台
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https://doi.org/10.1007/978-3-642-90692-3for both connection types in the setting of two-stage stochastic optimization. Our algorithms admit order-preserving metrics and thus significantly generalize and improve the allowed mutability of the metric in comparison to previous algorithms, which only allow scenario-dependent inflation factors.
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Local Search Based Approximation Algorithms for Two-Stage Stochastic Location Problems,for both connection types in the setting of two-stage stochastic optimization. Our algorithms admit order-preserving metrics and thus significantly generalize and improve the allowed mutability of the metric in comparison to previous algorithms, which only allow scenario-dependent inflation factors.
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https://doi.org/10.1007/978-3-642-90681-7orizontal or vertical pairs or constant length pairs on points laying on a grid. For pairs with no restriction we have an .-approximation algorithm and an .-approximation algorithm for the shortest separating planar graph.
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,Zentralnervensystem und perniziöse Anämie, sub-class of Second-Order Cone Programming. We show how to extend the multiplicative weights update method to derive approximation schemes for the above packing and covering problems. When the sets . are simple, such as ellipsoids or boxes, this yields substantial improvements in the running time over general convex programming solvers.
发表于 2025-3-28 00:54:40 | 显示全部楼层
https://doi.org/10.1007/978-3-642-90692-3sts a constant-factor approximation algorithm in three restricted cases: if the number of scenarios is fixed, if the number of missing vertices per scenario is bounded by a constant, and if the scenarios are nested. Finally, we discuss an elegant relation with an . minimum spanning tree problem.
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A Multiplicative Weights Update Algorithm for Packing and Covering Semi-infinite Linear Programs, sub-class of Second-Order Cone Programming. We show how to extend the multiplicative weights update method to derive approximation schemes for the above packing and covering problems. When the sets . are simple, such as ellipsoids or boxes, this yields substantial improvements in the running time over general convex programming solvers.
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