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研究生: 畢文豪
Pi, Wen-Hao
論文名稱: 部份終端點Steiner樹近似演算法之改進研究
An Improved Approximation of the Partial-Terminal Steiner Tree Problem
指導教授: 謝孫源
Hsieh, Sun-yuan
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2007
畢業學年度: 95
語文別: 英文
論文頁數: 34
中文關鍵詞: Steiner樹問題部份終端點Steiner樹問題MAX SNP-問題近似演算法
外文關鍵詞: MAX-SNP-hardness, the partial-terminal Steiner tree problem, The Steiner tree problem, approximation algorithms
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  • 給定一個圖G = (V,E),其中邊的長度必須要是正實數,以及一個端點子集R包含於V,所謂的Steiner樹,就是指要在圖G上找到一個連通的,而且沒有環路的子圖(即生成樹)並且包含了所有在R中的端點。Steiner樹問題就是要從圖G中找出一棵總成本花費最少的Steiner樹。
    類似Steiner樹,給定一個完全圖G = (V,E),其中邊的長度必須要是正有理數,以及兩個端點的子集R包含於V和R'包含於R,一個部份終端點Steiner樹也是一個Steiner樹,它包含了所有在R中的端點,並且所有在R'中的端點,在此Steiner樹中都必須在葉點上。這篇論文的主要問題就是要從圖G中找出一棵總成本花費最少的部份終端點Steiner樹。先前對於這個問題的最好的演算法近似比率為2p,其中p為目前最好的Steiner樹之演算法的近似比率。在這篇論文裡面,我們將改進這個演算法的近似比率從2p到2p–p/(3p–2)–e ,其中 e 的範圍為0至p–(p/(3p–2))之間。

    Given a graph G = (V,E) with a length (or cost) function c : E -> R+ on the edges, and a subset R is a subset of V of vertices, a Steiner tree is a connected and acyclic subgraph of G which contains all vertices in R. The Steiner tree problem is to find a Steiner tree of the minimum length in G.
    Like the Steiner tree, given a complete graph G = (V,E) with a length (or cost) function c : E -> Q¸ on the edges, and two proper subsets R is a subset of V and R' is a subset of R, a
    partial-terminal Steiner tree is a Steiner tree which contains all vertices in R such that all vertices in R' must be leaves. The partial-terminal Steiner tree problem is to find a partial-terminal Steiner tree of the minimum length in G. The previously best-known approximation ratio of the problem is 2p, where p is the approximation ratio of the Steiner tree problem. In this thesis, we improve the ratio from 2p to 2p - p/(3p-2)-e , where e is some non-negative number between 0 and p-p/(3p-2).

    Abstract(in Chinese) i Abstract ii Acknowledgement iii Contents vii List of Figures ix 1 Introduction 1 1.1 Motivation and Previous Survey . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Preliminaries 8 3 MAX SNP-hardness 12 4 An Approximation Algorithm 15 4.1 The Definition of Symbol delta . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 Case 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.3 Case 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.4 A Combined Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5 Discussion and Concluding Remarks 30

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