EDGE-DISJOINT SPANNING TREES OF ARBITRARY BOUNDED DIAMETER ON RANDOM INPUTS

We consider the following NP-hard generalization of the Minimum Spanning Tree problem. Given an undirected \(n\)-vertex edge-weighted complete graph and integers \(d\) and \(m\), find \(m\) edge-disjoint spanning trees of diameter at most \(d\) with minimum total weight. We propose a new polynomial-time approximation algorithm for the problem and study its performance guarantees on random inputs, that is, when the edge weights of the graph are i. i. d. random variables. We show that under mild conditions on the distribution parameters the proposed algorithm is asymptotically optimal for the case of continuous and discrete uniform distribution on \([a_n, b_n]\), \(a_n>0\), the shifted exponential distribution with shift \(a_n>0\), and distributions dominating the above. In contrast to a number of previous results for related problems, the new algorithm is asymptotically optimal not only if \(d\) tends to infinity with \(n\), but for constant \(d\) as well.

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Publication Details

Journal
Ural mathematical journal
Published
2025-12-27
DOI
https://doi.org/10.15826/umj.2025.2.007
Primary Topic
Complexity and Algorithms in Graphs
Type
article
Field-Weighted Citation Impact
0.00

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article

EDGE-DISJOINT SPANNING TREES OF ARBITRARY BOUNDED DIAMETER ON RANDOM INPUTS

Oxana Yu. Tsidulko, Edward Kh. Gimadi
Ural mathematical journal
Complexity and Algorithms in Graphs
article

EDGE-DISJOINT SPANNING TREES OF ARBITRARY BOUNDED DIAMETER ON RANDOM INPUTS

Oxana Yu. Tsidulko, Edward Kh. Gimadi
article en

Abstract

We consider the following NP-hard generalization of the Minimum Spanning Tree problem. Given an undirected \(n\)-vertex edge-weighted complete graph and integers \(d\) and \(m\), find \(m\) edge-disjoint spanning trees of diameter at most \(d\) with minimum total weight. We propose a new polynomial-time approximation algorithm for the problem and study its performance guarantees on random inputs, that is, when the edge weights of the graph are i. i. d. random variables. We show that under mild conditions on the distribution parameters the proposed algorithm is asymptotically optimal for the case of continuous and discrete uniform distribution on \([a_n, b_n]\), \(a_n>0\), the shifted exponential distribution with shift \(a_n>0\), and distributions dominating the above. In contrast to a number of previous results for related problems, the new algorithm is asymptotically optimal not only if \(d\) tends to infinity with \(n\), but for constant \(d\) as well.

Ural mathematical journalVol. 11(2)
Sobolev Institute of Mathematics (RU)
Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences
Openalex Percentile: Top 27%
Complexity and Algorithms in Graphs
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