Global optimization of non-convex piecewise linear regression splines
Multivariate adaptive regression spline (MARS) is a statistical modeling method used to represent a complex system. More recently, a version of MARS was modified to be piecewise linear. This paper presents a mixed integer linear program, called MARSOPT, that optimizes a non-convex piecewise linear M...
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| Veröffentlicht in: | Journal of global optimization 2017-07, Vol.68 (3), p.563-586 |
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| container_title | Journal of global optimization |
| container_volume | 68 |
| creator | Martinez, Nadia Anahideh, Hadis Rosenberger, Jay M. Martinez, Diana Chen, Victoria C. P. Wang, Bo Ping |
| description | Multivariate adaptive regression spline (MARS) is a statistical modeling method used to represent a complex system. More recently, a version of MARS was modified to be piecewise linear. This paper presents a mixed integer linear program, called MARSOPT, that optimizes a non-convex piecewise linear MARS model subject to constraints that include both linear regression models and piecewise linear MARS models. MARSOPT is customized for an automotive crash safety system design problem for a major US automaker and solved using branch and bound. The solutions from MARSOPT are compared with those from customized genetic algorithms. |
| doi_str_mv | 10.1007/s10898-016-0494-5 |
| format | Article |
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The solutions from MARSOPT are compared with those from customized genetic algorithms.</description><identifier>ISSN: 0925-5001</identifier><identifier>EISSN: 1573-2916</identifier><identifier>DOI: 10.1007/s10898-016-0494-5</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Computer Science ; Constraint modelling ; Customization ; Design engineering ; Genetic algorithms ; Global optimization ; Mathematical models ; Mathematics ; Mathematics and Statistics ; Mixed integer ; Operations Research/Decision Theory ; Optimization ; Real Functions ; Regression ; Regression analysis ; Safety ; Splines</subject><ispartof>Journal of global optimization, 2017-07, Vol.68 (3), p.563-586</ispartof><rights>Springer Science+Business Media New York 2017</rights><rights>COPYRIGHT 2017 Springer</rights><rights>Journal of Global Optimization is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c398t-b159f40b799e1481476091b28abe3e279f9e9be377166a2c6515d7d6a80c2b423</citedby><cites>FETCH-LOGICAL-c398t-b159f40b799e1481476091b28abe3e279f9e9be377166a2c6515d7d6a80c2b423</cites><orcidid>0000-0003-1935-7571</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10898-016-0494-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10898-016-0494-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27907,27908,41471,42540,51302</link.rule.ids></links><search><creatorcontrib>Martinez, Nadia</creatorcontrib><creatorcontrib>Anahideh, Hadis</creatorcontrib><creatorcontrib>Rosenberger, Jay M.</creatorcontrib><creatorcontrib>Martinez, Diana</creatorcontrib><creatorcontrib>Chen, Victoria C. 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| issn | 0925-5001 1573-2916 |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Computer Science Constraint modelling Customization Design engineering Genetic algorithms Global optimization Mathematical models Mathematics Mathematics and Statistics Mixed integer Operations Research/Decision Theory Optimization Real Functions Regression Regression analysis Safety Splines |
| title | Global optimization of non-convex piecewise linear regression splines |
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