Approximation Algorithms for Probabilistic Graphs
Kai Han
TL;DR
This paper investigates the complexity of k-median and k-center problems in probabilistic graphs, offering new algorithms with better approximation ratios than previous methods.
Contribution
It provides a detailed hardness analysis and introduces improved approximation algorithms for these problems in probabilistic graph settings.
Findings
Hardness results for k-median and k-center in probabilistic graphs
New algorithms with improved approximation ratios
Enhanced understanding of probabilistic graph optimization problems
Abstract
We study the k-median and k-center problems in probabilistic graphs. We analyze the hardness of these problems, and propose several algorithms with improved approximation ratios compared with the existing proposals.
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Data Management and Algorithms · Advanced Graph Theory Research