- Title
- Nonsmooth DC programming approach to clusterwise linear regression : Optimality conditions and algorithms
- Creator
- Bagirov, Adil; Ugon, Julien
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164107
- Identifier
- vital:12991
- Identifier
-
https://doi.org/10.1080/10556788.2017.1371717
- Identifier
- ISBN:1055-6788
- Abstract
- 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. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
- Publisher
- Taylor and Francis Ltd.
- Relation
- Optimization Methods and Software Vol. 33, no. 1 (2018), p. 194-219; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright © 2017 Informa UK Limited, trading as Taylor & Francis Group.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 0802 Computation Theory and Mathematics; Cluster analysis; DC programming; Nonsmooth optimization; Regression analysis
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