Data Mining Approach for Feature Based Parameter Tunning for Mixed-Integer Programming Solvers
Integer Programming (IP) is the most successful technique for solving hard combinatorial optimization problems. Modern IP solvers are very complex programs composed of many different procedures whose execution is embedded in the generic Branch & Bound framework. The activation of these procedure...
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| Vydáno v: | Procedia computer science Ročník 108; s. 715 - 724 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
2017
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| ISSN: | 1877-0509, 1877-0509 |
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| Abstract | Integer Programming (IP) is the most successful technique for solving hard combinatorial optimization problems. Modern IP solvers are very complex programs composed of many different procedures whose execution is embedded in the generic Branch & Bound framework. The activation of these procedures as well the definition of exploration strategies for the search tree can be done by setting different parameters. Since the success of these procedures and strategies in improving the performance of IP solvers varies widely depending on the problem being solved, the usual approach for discovering a good set of parameters considering average results is not ideal. In this work we propose a comprehensive approach for the automatic tuning of Integer Programming solvers where the characteristics of instances are considered. Computational experiments in a diverse set of 308 benchmark instances using the open source COIN-OR CBC solver were performed with different parameter sets and the results were processed by data mining algorithms. The results were encouraging: when trained with a portion of the database the algorithms were able to predict better parameters for the remaining instances in 84% of the cases. The selection of a single best parameter setting would provide an improvement in only 56% of instances, showing that great improvements can be obtained with our approach. |
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| AbstractList | Integer Programming (IP) is the most successful technique for solving hard combinatorial optimization problems. Modern IP solvers are very complex programs composed of many different procedures whose execution is embedded in the generic Branch & Bound framework. The activation of these procedures as well the definition of exploration strategies for the search tree can be done by setting different parameters. Since the success of these procedures and strategies in improving the performance of IP solvers varies widely depending on the problem being solved, the usual approach for discovering a good set of parameters considering average results is not ideal. In this work we propose a comprehensive approach for the automatic tuning of Integer Programming solvers where the characteristics of instances are considered. Computational experiments in a diverse set of 308 benchmark instances using the open source COIN-OR CBC solver were performed with different parameter sets and the results were processed by data mining algorithms. The results were encouraging: when trained with a portion of the database the algorithms were able to predict better parameters for the remaining instances in 84% of the cases. The selection of a single best parameter setting would provide an improvement in only 56% of instances, showing that great improvements can be obtained with our approach. |
| Author | Boas, Matheus G. Vilas Santos, Haroldo G. Merschmann, Luiz H.C. Martins, Rafael de S.O. |
| Author_xml | – sequence: 1 givenname: Matheus G. Vilas surname: Boas fullname: Boas, Matheus G. Vilas email: matheusgueedes91@gmail.com organization: Federal University of Ouro Preto, Department of Computing, Ouro Preto, Brazil – sequence: 2 givenname: Haroldo G. surname: Santos fullname: Santos, Haroldo G. email: haroldo.santos@gmail.com organization: Federal University of Ouro Preto, Department of Computing, Ouro Preto, Brazil – sequence: 3 givenname: Rafael de S.O. surname: Martins fullname: Martins, Rafael de S.O. email: martins.rso@gmail.com organization: Federal University of Ouro Preto, Department of Information Systems and Computing, João Monlevade, Brazil – sequence: 4 givenname: Luiz H.C. surname: Merschmann fullname: Merschmann, Luiz H.C. email: luiz.hcm@dcc.ufla.br organization: Federal University of Lavras, Department of Computer Science, Lavras, Brazil |
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| Cites_doi | 10.1016/j.ejor.2013.10.043 10.1147/rd.471.0057 10.1371/journal.pcbi.0020094 10.1145/1068009.1068194 10.2307/1910129 10.1007/s12532-011-0025-9 10.1109/72.870050 10.1137/040620886 10.1016/j.cor.2015.07.002 10.1023/A:1006559212014 |
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| Keywords | mixed-integer programming cut coin-or branch regression algorithms data mining feature based parameter tunning |
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| References | Lougee-Heimer (bib0013) 2003; 47 Joseph Haas, Maxim Peysakhov, and Spiros Mancoridis. Ga-based Parameter Tuning for Multi-agent Systems. In Vapnik (bib0016) 1995 Kohavi, John (bib0011) 1995 Lpez-Ibez, Sttzle (bib0014) 2014; 235 Bellio, Ceschia, Gaspero, Schaerf, Urli (bib0005) 2016; 65 Achard, De Schutter (bib0001) 07 2006; 2 Mustafa Baz, Brady Hunsaker, P Brooks, and Abhijit Gosavi. Automated Tuning of Optimization Software Parameters. Technical report, Technical Report TR2007-7, University of Pittsburgh, Department of Industrial Engineering, 2007. Land, Doig (bib0012) 1960; 28 Bixby, Fenelon, Gu, Rothberg, Wunderling (bib0006) 2004 Koch, Achterberg, Andersen, Bastert, Berthold, Bixby, Danna, Gamrath, Gleixner, Heinz (bib0010) 2011; 3 Michael R. Garey and David S. Johnson. GECCO ‘05, pages 1085–1086, New York, NY, USA, 2005. ACM. Witten, Frank, Hall (bib0017) 2011 Shevade, Keerthi, Bhattacharyya, Murthy (bib0015) 2000; 11 Atkeson, Moore, Schaal (bib0002) 1997 Forrest, Lougee-Heimer (bib0007) 2005 W. H. Freeman & Co., New York, NY, USA, 1979. Audet, Orban (bib0003) 2006; 17 Bixby (10.1016/j.procs.2017.05.286_bib0006) 2004 Forrest (10.1016/j.procs.2017.05.286_bib0007) 2005 10.1016/j.procs.2017.05.286_bib0004 Witten (10.1016/j.procs.2017.05.286_bib0017) 2011 Achard (10.1016/j.procs.2017.05.286_bib0001) 2006; 2 10.1016/j.procs.2017.05.286_bib0009 10.1016/j.procs.2017.05.286_bib0008 Land (10.1016/j.procs.2017.05.286_bib0012) 1960; 28 Lougee-Heimer (10.1016/j.procs.2017.05.286_bib0013) 2003; 47 Lpez-Ibez (10.1016/j.procs.2017.05.286_bib0014) 2014; 235 Audet (10.1016/j.procs.2017.05.286_bib0003) 2006; 17 Bellio (10.1016/j.procs.2017.05.286_bib0005) 2016; 65 Koch (10.1016/j.procs.2017.05.286_bib0010) 2011; 3 Shevade (10.1016/j.procs.2017.05.286_bib0015) 2000; 11 Kohavi (10.1016/j.procs.2017.05.286_bib0011) 1995 Atkeson (10.1016/j.procs.2017.05.286_bib0002) 1997 Vapnik (10.1016/j.procs.2017.05.286_bib0016) 1995 |
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Freeman & Co., New York, NY, USA, 1979. – reference: Michael R. Garey and David S. Johnson. – volume: 3 year: 2011 ident: bib0010 article-title: MIPLIB 2010 publication-title: Mathematical Programming Computation – volume: 47 start-page: 57 year: 2003 end-page: 66 ident: bib0013 article-title: The Common Optimization Interface for Operations Research: Promoting Open-source Software in the Operations Research Community publication-title: IBM Journal of Research and Development – start-page: 309 year: 2004 end-page: 325 ident: bib0006 article-title: Mixed Integer Programming: A Progress Report publication-title: The sharpest cut: the impact of Manfred Padberg and his work, chapter 18 – reference: , GECCO ‘05, pages 1085–1086, New York, NY, USA, 2005. ACM. – volume: 2 start-page: 1 year: 07 2006 end-page: 11 ident: bib0001 article-title: Complex Parameter Landscape for a Complex Neuron Model publication-title: PLOS Computational Biology – reference: Mustafa Baz, Brady Hunsaker, P Brooks, and Abhijit Gosavi. Automated Tuning of Optimization Software Parameters. Technical report, Technical Report TR2007-7, University of Pittsburgh, Department of Industrial Engineering, 2007. – start-page: 257 year: 2005 end-page: 277 ident: bib0007 article-title: Cbc User Guide publication-title: Emerging Theory, Methods, and Applications – reference: Joseph Haas, Maxim Peysakhov, and Spiros Mancoridis. Ga-based Parameter Tuning for Multi-agent Systems. In – volume: 11 year: 2000 ident: bib0015 article-title: Improvements to the SMO Algorithm for SVM Regression publication-title: IEEE Transactions on Neural Networks – start-page: 11 year: 1997 end-page: 73 ident: bib0002 article-title: Locally Weighted Learning publication-title: Artificial Intelligence Review – start-page: 304 year: 1995 end-page: 312 ident: bib0011 article-title: Automatic Parameter Selection by Minimizing Estimated Error publication-title: In Proceedings of the Twelfth International Conference on Machine Learning – volume: 28 start-page: 497 year: 1960 end-page: 520 ident: bib0012 article-title: An Automatic Method for Solving Discrete Programming Problems publication-title: Econometrica – ident: 10.1016/j.procs.2017.05.286_bib0004 – volume: 235 start-page: 569 issue: 3 year: 2014 ident: 10.1016/j.procs.2017.05.286_bib0014 article-title: Automatically Improving the Anytime Behaviour of Optimisation Algorithms publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2013.10.043 – volume: 47 start-page: 57 issue: 1 year: 2003 ident: 10.1016/j.procs.2017.05.286_bib0013 article-title: The Common Optimization Interface for Operations Research: Promoting Open-source Software in the Operations Research Community publication-title: IBM Journal of Research and Development doi: 10.1147/rd.471.0057 – volume: 2 start-page: 1 issue: 7 year: 2006 ident: 10.1016/j.procs.2017.05.286_bib0001 article-title: Complex Parameter Landscape for a Complex Neuron Model publication-title: PLOS Computational Biology doi: 10.1371/journal.pcbi.0020094 – year: 1995 ident: 10.1016/j.procs.2017.05.286_bib0016 – ident: 10.1016/j.procs.2017.05.286_bib0008 – ident: 10.1016/j.procs.2017.05.286_bib0009 doi: 10.1145/1068009.1068194 – volume: 28 start-page: 497 year: 1960 ident: 10.1016/j.procs.2017.05.286_bib0012 article-title: An Automatic Method for Solving Discrete Programming Problems publication-title: Econometrica doi: 10.2307/1910129 – volume: 3 issue: 2 year: 2011 ident: 10.1016/j.procs.2017.05.286_bib0010 article-title: MIPLIB 2010 publication-title: Mathematical Programming Computation doi: 10.1007/s12532-011-0025-9 – start-page: 304 year: 1995 ident: 10.1016/j.procs.2017.05.286_bib0011 article-title: Automatic Parameter Selection by Minimizing Estimated Error – year: 2011 ident: 10.1016/j.procs.2017.05.286_bib0017 – volume: 11 issue: 5 year: 2000 ident: 10.1016/j.procs.2017.05.286_bib0015 article-title: Improvements to the SMO Algorithm for SVM Regression publication-title: IEEE Transactions on Neural Networks doi: 10.1109/72.870050 – volume: 17 start-page: 642 issue: 3 year: 2006 ident: 10.1016/j.procs.2017.05.286_bib0003 article-title: Finding Optimal Algorithmic Parameters Using Derivative-Free Optimization publication-title: SIAM Journal on Optimization doi: 10.1137/040620886 – start-page: 309 year: 2004 ident: 10.1016/j.procs.2017.05.286_bib0006 article-title: Mixed Integer Programming: A Progress Report – start-page: 257 year: 2005 ident: 10.1016/j.procs.2017.05.286_bib0007 article-title: Cbc User Guide – volume: 65 start-page: 83 year: 2016 ident: 10.1016/j.procs.2017.05.286_bib0005 article-title: Feature-based Tuning of Simulated Annealing applied to the Curriculum-based Course Timetabling Problem publication-title: Computers & Operations Research doi: 10.1016/j.cor.2015.07.002 – start-page: 11 year: 1997 ident: 10.1016/j.procs.2017.05.286_bib0002 article-title: Locally Weighted Learning publication-title: Artificial Intelligence Review doi: 10.1023/A:1006559212014 |
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| Snippet | Integer Programming (IP) is the most successful technique for solving hard combinatorial optimization problems. Modern IP solvers are very complex programs... |
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| Title | Data Mining Approach for Feature Based Parameter Tunning for Mixed-Integer Programming Solvers |
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