MINLP formulations for continuous piecewise linear function fitting - Noam Goldberg; Steffen Rebennack; Youngdae Kim; Vitaliy Krasko; Sven Leyffer (Computational Optimization and Applications, 2021)
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.
Keywords: 510; Mixed-integer nonlinear program; Linear spline regression; Branch-and-bound; Reformulation