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Automatic Tuning of the SkewedKruskal Algorithm

In: Theory, Algorithms, and Experiments in Applied Optimization

Author

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  • Mattia Lecchi

    (University of Milan)

  • Giovanni Righini

    (University of Milan)

Abstract

The SkewedKruskal algorithm is an implementation of Kruskal algorithm where the edge list is recursively sorted on demand, mimicking the well-known QuickSort algorithm. We investigate the issue of improving the time performance of SkewedKruskal by guessing the position of the largest edge of a minimum cost spanning tree (MST), in order to avoid sorting unnecessary edges. For this purpose, a statistical analysis is performed on the distribution of the edge weights in some classes of randomly generated weighted graphs, so that the position of the largest MST edge can be guessed by sampling a relatively small number of edge weights. Experimental results are reported to evaluate the effectiveness of the techniques proposed.

Suggested Citation

  • Mattia Lecchi & Giovanni Righini, 2025. "Automatic Tuning of the SkewedKruskal Algorithm," Springer Optimization and Its Applications, in: Boris Goldengorin (ed.), Theory, Algorithms, and Experiments in Applied Optimization, pages 171-198, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-91357-0_9
    DOI: 10.1007/978-3-031-91357-0_9
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