On the Derivation of Continuous Piecewise Linear Approximating Functions

Lingxun Kong () and Christos T. Maravelias ()
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Lingxun Kong: Department of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706
Christos T. Maravelias: Department of Chemical and Biological Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706

INFORMS Journal on Computing, 2020, vol. 32, issue 3, 531-546

Abstract: We propose mixed-integer programming models for fitting univariate discrete data points with continuous piecewise linear (PWL) functions. The number of approximating function segments and the locations of break points are optimized simultaneously. The proposed models include linear constraints and convex objective function and, thus, are computationally more efficient than previously proposed mixed-integer nonlinear programming models. We also show how the proposed models can be extended to approximate univariate functions with PWL functions with the minimum number of segments subject to bounds on the pointwise error.

Keywords: mixed-integer programming; data fitting; large-scale (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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