Using Piecewise Linear Functions for Solving MINLPs

Björn Geißler, Alexander Martin, Antonio Morsi, Lars Schewe

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract / Description of output

In this chapter we want to demonstrate that in certain cases general mixed integer nonlinear programs (MINLPs) can be solved by just applying purely techniques from the mixed integer linear world. The way to achieve this is to approximate the nonlinearities by piecewise linear functions. The advantage of applying mixed integer lin- ear techniques are that these methods are nowadays very mature, that is, they are fast, robust, and are able to solve problems with up to millions of variables. In addition, these methods have the potential of finding globally optimal solutions or at least to provide solution guarantees. On the other hand, one tends to say at this point “If you have a hammer, everything is a nail.”[15], because one tries to reformulate or to approximate an ac- tual nonlinear problem until one obtains a model that is tractable by the methods one is common with. Besides the fact that this is a very typical approach in mathematics the question stays whether this is a reasonable approach for the solution of MINLPs or whether the nature of the nonlin- earities inherent to the problem gets lost and the solutions obtained from the mixed integer linear problem have no meaning for the MINLP. The purpose of this chapter is to discuss this question. We will see that the truth lies somewhere in between and that there are problems where this is indeed a reasonable way to go and others where it is not.
Original languageEnglish
Title of host publicationMixed Integer Nonlinear Programming
EditorsLeyffer Sven, Lee Jon
PublisherSpringer Science+Business Media, New York
Number of pages28
ISBN (Print)978-1-4614-1926-6
Publication statusPublished - 2012

Publication series

NameThe IMA Volumes in Mathematics and its Applications
PublisherSpringer Science+Business Media, New York


Dive into the research topics of 'Using Piecewise Linear Functions for Solving MINLPs'. Together they form a unique fingerprint.

Cite this