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Nonsmooth DC programming approach to clusterwise linear regression: optimality conditions and algorithms
Version 2 2024-06-04, 13:49Version 2 2024-06-04, 13:49
Version 1 2018-04-16, 16:03Version 1 2018-04-16, 16:03
journal contribution
posted on 2024-06-04, 13:49 authored by AM 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.
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Journal
Optimization methods and softwareVolume
33Pagination
194-219Location
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 LimitedIssue
1Publisher
Taylor & FrancisUsage metrics
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