Tree edge decomposition with an application to minimum ultrametric tree approximation
Chia-Mao Huang (),
Bang Ye Wu () and
Chang-Biau Yang ()
Additional contact information
Chia-Mao Huang: National Sun Yat-sen University
Bang Ye Wu: Shu-Te University, YenChau
Chang-Biau Yang: National Sun Yat-sen University
Journal of Combinatorial Optimization, 2006, vol. 12, issue 3, No 3, 217-230
Abstract:
Abstract A k-decomposition of a tree is a process in which the tree is recursively partitioned into k edge-disjoint subtrees until each subtree contains only one edge. We investigated the problem how many levels it is sufficient to decompose the edges of a tree. In this paper, we show that any n-edge tree can be 2-decomposed (and 3-decomposed) within at most ⌈1.44 log n⌉ (and ⌈log n⌉ respectively) levels. Extreme trees are given to show that the bounds are asymptotically tight. Based on the result, we designed an improved approximation algorithm for the minimum ultrametric tree.
Keywords: Tree; Decomposition; Ultrametric; Approximation algorithm (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10878-006-9626-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:12:y:2006:i:3:d:10.1007_s10878-006-9626-z
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878
DOI: 10.1007/s10878-006-9626-z
Access Statistics for this article
Journal of Combinatorial Optimization is currently edited by Thai, My T.
More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().