Titlebook: Algorithmic Aspects in Information and Management; 18th International C Smita Ghosh,Zhao Zhang Conference proceedings 2024 The Editor(s) (i

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王得到

Representation of?Dominating Set Variants Using Dataless Neural Networks Set problem is to find a Dominating Set of minimum cardinality in a given graph .. It is a well studied problem in the literature and known to be .. In this paper, we explore the Dominating Set problem and one of its variants known as the Vertex-Edge Dominating Set problem through the lens of datal

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Algorithms for?the?Constrained Assignment Problems with?Bounds and?Maximum Penalty the network edge servers for data processing, this process is called .. In order to use the network edge servers more efficiently, it is usually expected that the number of objects served by any edge server will exceed a certain number and also be controlled within a certain number..In order to eff

Arboreal

Brain-Waves

Speeding Up Constrained ,-Means Through 2-Meansn . can induce a .-approximation for the problem. The existing approximation scheme for the constrained 2-means problem takes . time, and the existing approximation scheme for the constrained .-means problem takes . time. Using the method developed in this paper, we point out that every existing app

FILTH

DAUNT

門(mén)窗的側(cè)柱

On the?Kalai-Smorodinsky Solutions for?Bi-objective Spanning Tree Problemications. In this problem, each edge of a given graph is associated with two distinct weights, and the objective is to find a spanning tree that simultaneously minimizes both total weights. As a specific case of bi-objective optimization, we may be interested in enumerating all the Pareto-optimal so

Chandelier

山頂可休息

Time-Sensitive Target Coverage Under Visibility Constraints with?UAVsurveillance. However, in real-world scenarios, targets are often obscured by obstacles or other targets, resulting in a limited visible region. Furthermore, in some scenarios, such as surveillance and disaster management, UAVs have to cover the targets within a certain time since the information of