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Titlebook: Algorithmic Aspects in Information and Management; 14th International C Zhao Zhang,Wei Li,Ding-Zhu Du Conference proceedings 2020 Springer

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樓主: Cyclone
31#
發(fā)表于 2025-3-27 00:04:30 | 只看該作者
32#
發(fā)表于 2025-3-27 01:36:09 | 只看該作者
Denkfallen und Programmierfehler two stage, finite scenarios stochastic versions of set cover problem with submodular penalties which is the generalization of the stochastic vertex cover problem with submodular penalties. The goal is to minimize the sum of the first stage cost, the expected second stage cost and the expected penal
33#
發(fā)表于 2025-3-27 07:46:28 | 只看該作者
https://doi.org/10.1007/978-3-642-75324-4 plane ., we are asked to find the location of a line . and a Steiner tree .(.), which consists of vertical and horizontal line segments plus the line ., to interconnect these . points and at least one point on the line ., the objective is to minimize total weight of .(.), ., . | .(.) is a Steiner t
34#
發(fā)表于 2025-3-27 11:14:19 | 只看該作者
35#
發(fā)表于 2025-3-27 15:18:42 | 只看該作者
36#
發(fā)表于 2025-3-27 18:27:13 | 只看該作者
https://doi.org/10.1007/978-3-658-21663-4e . and an independent set of size ., such that each node in the independent set is adjacent to exactly one node in the clique. For various optimization objective functions studied in the literature, we present improved hardness and approximation results.
37#
發(fā)表于 2025-3-28 01:55:38 | 只看該作者
38#
發(fā)表于 2025-3-28 02:35:47 | 只看該作者
39#
發(fā)表于 2025-3-28 08:40:37 | 只看該作者
https://doi.org/10.1007/978-3-658-11983-6f clients, a budget . and a lower bound .. Every facility is associated with a weight. Every facility-client pair is associated with a connection cost. The aim is to select a subset of facilities to open and connect every client to some opened facility, such that the total weights of the selected fa
40#
發(fā)表于 2025-3-28 12:58:14 | 只看該作者
Sebastian Horn,Dennis Thorwarthional unit sphere ., and an integer ., it aims to partition the data set . into . sets so as to minimize the sum of cosine dissimilarity measure from each data point to its closest center. We present a constant expected approximation guarantee for this problem based on integrating the .-means++ seed
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