- Title
- A difference of convex optimization algorithm for piecewise linear regression
- Creator
- Bagirov, Adil; Taheri, Sona; Asadi, Soodabeh
- Date
- 2019
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/168543
- Identifier
- vital:13829
- Identifier
-
https://doi.org/10.3934/jimo.2018077
- Identifier
- ISBN:1547-5816
- Abstract
- The problem of finding a continuous piecewise linear function approximating a regression function is considered. This problem is formulated as a nonconvex nonsmooth optimization problem where the objective function is represented as a difference of convex (DC) functions. Subdifferentials of DC components are computed and an algorithm is designed based on these subdifferentials to find piecewise linear functions. The algorithm is tested using some synthetic and real world data sets and compared with other regression algorithms.
- Publisher
- American Institute of Mathematical Sciences
- Relation
- Journal of Industrial and Management Optimization Vol. 15, no. 2 (2019), p. 909-932; http://purl.org/au-research/grants/arc/DP140103213
- Rights
- Copyright © 2019 American Institute of Mathematical Sciences.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 0801 Artificial Intelligence and Image Processing; DC optimization; Nonconvex optimization; Nonsmooth optimization; Regression analysis; Subdifferential
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