MINLP formulations for continuous piecewise linear function fitting

被引:0
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作者
Noam Goldberg
Steffen Rebennack
Youngdae Kim
Vitaliy Krasko
Sven Leyffer
机构
[1] Bar-Ilan University,
[2] Karlsruhe Institute of Technology,undefined
[3] Argonne National Laboratory,undefined
[4] Colorado School of Mines,undefined
[5] Argonne National Laboratory,undefined
关键词
Mixed-integer nonlinear program; Linear spline regression; Branch-and-bound; Reformulation;
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摘要
We consider a nonconvex mixed-integer nonlinear programming (MINLP) model proposed by Goldberg et al. (Comput Optim Appl 58:523–541, 2014. https://doi.org/10.1007/s10589-014-9647-y) for piecewise linear function fitting. We show that this MINLP model is incomplete and can result in a piecewise linear curve that is not the graph of a function, because it misses a set of necessary constraints. We provide two counterexamples to illustrate this effect, and propose three alternative models that correct this behavior. We investigate the theoretical relationship between these models and evaluate their computational performance.
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页码:223 / 233
页数:10
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