Markov chain monte carlo and the traveling salesman problem.
by Liang Fa Ming. === Publication date from spine. === Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. === Includes bibliographical references (leaves 49-53). === ABSTRACT --- p.1 === Chapter CHAPTER 1 : --- Introduction --- p.2 === Chapter 1.1 : --- The TSP Problem --- p.2 === Chapter 1...
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ndltd-cuhk.edu.hk-oai-cuhk-dr-cuhk_3214982019-02-19T03:56:32Z Markov chain monte carlo and the traveling salesman problem. Markov processes Monte Carlo method Traveling salesman problem by Liang Fa Ming. Publication date from spine. Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. Includes bibliographical references (leaves 49-53). ABSTRACT --- p.1 Chapter CHAPTER 1 : --- Introduction --- p.2 Chapter 1.1 : --- The TSP Problem --- p.2 Chapter 1.2: --- Application --- p.3 Chapter CHAPTER 2 : --- Review of Exact and Approximate Algorithms for TSP --- p.4 Chapter 2.1 : --- Exact Algorithm --- p.4 Chapter 2.2 : --- Heuristic Algorithms --- p.8 Chapter CHAPTER 3 : --- Markov Chain Monte Carlo Methods --- p.16 Chapter 3.1: --- Markov Chain Monte Carlo --- p.16 Chapter 3.2 : --- Conditioning and Gibbs Sampler --- p.17 Chapter 3.3: --- The Metropolis-Hasting Algorithm --- p.18 Chapter 3.4: --- Auxiliary Variable Methods --- p.21 Chapter CHAPTER 4: --- Weighted Markov Chain Monte Carlo Method --- p.24 Chapter CHAPTER 5 : --- Traveling Salesman Problem --- p.31 Chapter 5.1: --- Buildup Order --- p.33 Chapter 5.2: --- Path Construction through a Group of Points --- p.34 Chapter 5.3: --- Solving TSP Using the Weighted Markov Chain Method --- p.38 Chapter 5.4: --- Temperature Scheme --- p.40 Chapter 5.5 : --- How to Adjust the Constant Prior-Ratio --- p.41 Chapter 5.6: --- Validation of Our Algorithm by a Simple Example --- p.41 Chapter 5.7 : --- Adding/Deleting Blockwise --- p.42 Chapter 5.8: --- The sequential Optimal Method and Post Optimization --- p.43 Chapter 5. 9 : --- Composite Algorithm --- p.44 Chapter 5.10: --- Numerical Comparisons and Tests --- p.45 Chapter CHAPTER 6 : --- Conclusion --- p.48 REFERENCES --- p.49 APPENDIX A --- p.54 APPENDIX B --- p.58 APPENDIX C --- p.61 Chinese University of Hong Kong Liang, F. (Faming) , 1970- Chinese University of Hong Kong Graduate School. Division of Statistics. [1996] 1996 Text bibliography print 65 leaves : ill. ; 30 cm. cuhk:321498 http://library.cuhk.edu.hk/record=b5888984 eng Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) http://repository.lib.cuhk.edu.hk/en/islandora/object/cuhk%3A321498/datastream/TN/view/Markov%20chain%20monte%20carlo%20and%20the%20traveling%20salesman%20problem.jpghttp://repository.lib.cuhk.edu.hk/en/item/cuhk-321498 |
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Markov processes Monte Carlo method Traveling salesman problem |
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Markov processes Monte Carlo method Traveling salesman problem Markov chain monte carlo and the traveling salesman problem. |
description |
by Liang Fa Ming. === Publication date from spine. === Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. === Includes bibliographical references (leaves 49-53). === ABSTRACT --- p.1 === Chapter CHAPTER 1 : --- Introduction --- p.2 === Chapter 1.1 : --- The TSP Problem --- p.2 === Chapter 1.2: --- Application --- p.3 === Chapter CHAPTER 2 : --- Review of Exact and Approximate Algorithms for TSP --- p.4 === Chapter 2.1 : --- Exact Algorithm --- p.4 === Chapter 2.2 : --- Heuristic Algorithms --- p.8 === Chapter CHAPTER 3 : --- Markov Chain Monte Carlo Methods --- p.16 === Chapter 3.1: --- Markov Chain Monte Carlo --- p.16 === Chapter 3.2 : --- Conditioning and Gibbs Sampler --- p.17 === Chapter 3.3: --- The Metropolis-Hasting Algorithm --- p.18 === Chapter 3.4: --- Auxiliary Variable Methods --- p.21 === Chapter CHAPTER 4: --- Weighted Markov Chain Monte Carlo Method --- p.24 === Chapter CHAPTER 5 : --- Traveling Salesman Problem --- p.31 === Chapter 5.1: --- Buildup Order --- p.33 === Chapter 5.2: --- Path Construction through a Group of Points --- p.34 === Chapter 5.3: --- Solving TSP Using the Weighted Markov Chain Method --- p.38 === Chapter 5.4: --- Temperature Scheme --- p.40 === Chapter 5.5 : --- How to Adjust the Constant Prior-Ratio --- p.41 === Chapter 5.6: --- Validation of Our Algorithm by a Simple Example --- p.41 === Chapter 5.7 : --- Adding/Deleting Blockwise --- p.42 === Chapter 5.8: --- The sequential Optimal Method and Post Optimization --- p.43 === Chapter 5. 9 : --- Composite Algorithm --- p.44 === Chapter 5.10: --- Numerical Comparisons and Tests --- p.45 === Chapter CHAPTER 6 : --- Conclusion --- p.48 === REFERENCES --- p.49 === APPENDIX A --- p.54 === APPENDIX B --- p.58 === APPENDIX C --- p.61 |
author2 |
Liang, F. (Faming) , 1970- |
author_facet |
Liang, F. (Faming) , 1970- |
title |
Markov chain monte carlo and the traveling salesman problem. |
title_short |
Markov chain monte carlo and the traveling salesman problem. |
title_full |
Markov chain monte carlo and the traveling salesman problem. |
title_fullStr |
Markov chain monte carlo and the traveling salesman problem. |
title_full_unstemmed |
Markov chain monte carlo and the traveling salesman problem. |
title_sort |
markov chain monte carlo and the traveling salesman problem. |
publisher |
Chinese University of Hong Kong |
publishDate |
[199 |
url |
http://library.cuhk.edu.hk/record=b5888984 http://repository.lib.cuhk.edu.hk/en/item/cuhk-321498 |
_version_ |
1718980121829834752 |