The k-center problem, a well-known graph theory challenge, remains NP-hard, prompting the need for modified algorithms like a sampling variant of the greedy approach.
We adapt the traditional greedy algorithm into a sampling method to tackle the k-center problem, utilizing shortest-path distances to guide the selection process.
Our modified greedy algorithm, which relies on distance sampling, maintains a running time complexity of O(k) when distances are pre-computed.
The k-center problem’s computational difficulty necessitates alternative strategies, such as our approach combining traditional techniques with probabilistic sampling for optimal results.
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