Nonsmooth DC programming approach to clusterwise linear regression: optimality conditions and algorithms
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journal contribution
posted on 2018-01-01, 00:00 authored by A M Bagirov, Julien UgonJulien Ugon© 2017 Informa UK Limited, trading as Taylor & Francis Group. The clusterwise linear regression problem is formulated as a nonsmooth nonconvex optimization problem using the squared regression error function. The objective function in this problem is represented as a difference of convex functions. Optimality conditions are derived, and an algorithm is designed based on such a representation. An incremental approach is proposed to generate starting solutions. The algorithm is tested on small to large data sets.
History
Journal
Optimization methods and softwareVolume
33Issue
1Pagination
194 - 219Publisher
Taylor & FrancisLocation
Abingdon, Eng.Publisher DOI
ISSN
1055-6788eISSN
1029-4937Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2017, Informa UK LimitedUsage metrics
Keywords
Science & TechnologyTechnologyPhysical SciencesComputer Science, Software EngineeringOperations Research & Management ScienceMathematics, AppliedComputer ScienceMathematicsnonsmooth optimizationDC programmingregression analysiscluster analysisOPTIMIZATIONMETHODOLOGYComputation Theory and Mathematics
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