Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7664
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dc.contributor.authorLi Y.Y.en_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorWong C.K.en_US
dc.date.accessioned2023-03-29T07:01:50Z-
dc.date.available2023-03-29T07:01:50Z-
dc.date.issued2000-
dc.identifier.citationJournal of Combinatorial Optimization, 2000, vol. 4 (1), pp. 79 - 98en_US
dc.identifier.issn13826905-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7664-
dc.description.abstractWe consider Steiner minimum trees (SMT) in the plane, where only orientations with angle iπ/σ, 0 ≤ i ≤; σ - 1 and σ an integer, are allowed. The orientations define a metric, called the orientation metric, λσ, in a natural way. In particular, λ2 metric is the rectilinear metric and the Euclidean metric can be regarded as λ∞ metric. In this paper, we provide a method to find an optimal λσ SMT for 3 or 4 points by analyzing the topology of λσ SMT's in great details. Utilizing these results and based on the idea of loop detection first proposed in Chao and Hsu, IEEE Trans. CAD, vol. 13, no. 3, pp. 303-309, 1994, we further develop an O(n2) time heuristic for the general λσ SMT problem, including the Euclidean metric. Experiments performed on publicly available benchmark data for 12 different metrics, plus the Euclidean metric, demonstrate the efficiency of our algorithms and the quality of our results.en_US
dc.language.isoenen_US
dc.publisherSpringer Netherlandsen_US
dc.relation.ispartofJournal of Combinatorial Optimizationen_US
dc.titleEfficient Heuristics for Orientation Metric and Euclidean Steiner Tree Problemsen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1023/A:1009837006569-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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