A difference of convex optimization algorithm for piecewise linear regression
Adil Bagirov Sona Taheri Soodabeh Asadi
Journal of Industrial & Management Optimization 2019, 15(2): 909-932 doi: 10.3934/jimo.2018077

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.

keywords: Regression analysis nonsmooth optimization nonconvex optimization DC optimization subdifferential

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