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Permanent link (DOI): https://doi.org/10.7939/R3SN01G26

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Approximation Algorithms under the Worst-case Analysis and the Smoothed Analysis Open Access

Descriptions

Other title
Subject/Keyword
Smoothed analysis
Approximation algorithm
Worst-case analysis
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Tong, Weitian
Supervisor and department
Lin, Guohui (Computing Science)
Examining committee member and department
Szepesvari, Csaba (Computing Science)
Salavatipour, Mohammad (Computing Science)
Lin, Guohui (Computing Science)
Greiner, Russell (Computing Science)
Goebel, Randy (Computing Science)
Gu, Qianping (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2015-07-22T15:32:46Z
Graduation date
2015-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
How to evaluate the performance of an algorithm is a very important subject in computer science, for understanding its applicability, for understanding the problem which it is applied to, and for the development of new ideas that help to improve the existing algorithms. There are two main factors, i.e. the performance measure and the analysis model, that affect the evaluation of an algorithm. In this thesis, we analyse algorithms with approximation ratio as a measure under the worst-case analysis and the smoothed analysis. In particular, several interesting discrete optimization problems from bioinformatics, networking and graph theory are investigated, and currently the best approximation algorithms are designed for them under the classic analysis model — the worst-case analysis and the more ``realistic" model — the smoothed analysis.
Language
English
DOI
doi:10.7939/R3SN01G26
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
L. Huang, W. Tong, R. Goebel, T. Liu, and G. Lin. A 0:5358-approximation for Bandpass-2. Journal of Combinatorial Optimization, pages 1–15, 2013.W. Tong, Z.-Z. Chen, L. Wang, Y. Xu, J. Xu, R. Goebel, and G. Lin. An approximation algorithm for the Bandpass-2 problem. CoRR, abs/1307.7089, 2013.W. Tong, R. Goebel, W. Ding, and G. Lin. An improved approximation algorithm for the Bandpass problem. In FAW-AAIM, LNCS 7285, pages 351–358, 2012.W. Tong, R. Goebel, T. Liu, and G. Lin. Approximation algorithms for the maximum multiple RNA interaction problem. In COCOA, LNCS 8287, pages 49–59, 2013.W. Tong, R. Goebel, T. Liu, and G. Lin. Approximating the maximum multiple RNA interaction problem. Theoretical Computer Science, 556:63–70, 2014.Z. Chen, B. Fu, R. Goebel, G. Lin, W. Tong, J. Xu, B. Yang, Z. Zhao, and B. Zhu. On the approximability of the exemplar adjacency number problem for genomes with gene repetitions. Theoretical Computer Science, 50:59–65, 2014.W. Tong and G. Lin. An improved approximation algorithm for the minimum common integer partition problem. In ISAAC, pages 353–364, 2014.W. Tong, R. Goebel, and G. Lin. Approximating the minimum independent dominating set in perturbed graphs. In COCOON, LNCS 7936, pages 257–267, 2013.W. Tong, R. Goebel, and G. Lin. Approximating the minimum independent dominating set in perturbed graphs. Theoretical Computer Science, 554:275–282, 2014.W. Tong, R. Goebel, and G. Lin. On the smoothed heights of Trie and Patricia index trees. In COCOON, pages 94–103, 2014.

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