Creditee
发表于 2025-3-27 00:04:30
http://reply.papertrans.cn/16/1529/152885/152885_31.png
enhance
发表于 2025-3-27 01:36:09
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
摊位
发表于 2025-3-27 07:46:28
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
盟军
发表于 2025-3-27 11:14:19
http://reply.papertrans.cn/16/1529/152885/152885_34.png
Kidney-Failure
发表于 2025-3-27 15:18:42
http://reply.papertrans.cn/16/1529/152885/152885_35.png
Medicare
发表于 2025-3-27 18:27:13
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.
palpitate
发表于 2025-3-28 01:55:38
http://reply.papertrans.cn/16/1529/152885/152885_37.png
Throttle
发表于 2025-3-28 02:35:47
http://reply.papertrans.cn/16/1529/152885/152885_38.png
Ballad
发表于 2025-3-28 08:40:37
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
badinage
发表于 2025-3-28 12:58:14
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