- Title
- DC programming algorithm for clusterwise linear L1 regression
- Creator
- Bagirov, Adil; Taheri, Sona
- Date
- 2017
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/158400
- Identifier
- vital:11758
- Identifier
- http://doi.org/10.1007/s40305-017-0151-9
- Identifier
- ISSN:2194-668X
- Abstract
- The aim of this paper is to develop an algorithm for solving the clusterwise linear least absolute deviations regression problem. This problem is formulated as a nonsmooth nonconvex optimization problem, and the objective function is represented as a difference of convex functions. Optimality conditions are derived by using this representation. An algorithm is designed based on the difference of convex representation and an incremental approach. The proposed algorithm is tested using small to large artificial and real-world data sets. © 2017, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag Berlin Heidelberg.
- Publisher
- Springer Berlin Heidelberg
- Relation
- Journal of the Operations Research Society of China Vol. 5, no. 2 (2017), p. 233-256; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright © 2017, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag Berlin Heidelberg.
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Clusterwise regression; Incremental algorithm; Nonsmooth optimization; Smoothing; 0102 Applied Mathematics
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