Solving Mixed-Integer Nonlinear Programs using Adaptively Refined Mixed-Integer Linear Programs

  • We propose a method for solving mixed-integer nonlinear programs (MINLPs) to global optimality by discretization of occuring nonlinearities. The main idea is based on using piecewise linear functions to construct mixed-integer linear program (MIP) relaxations of the underlying MINLP. In order to find a global optimum of the given MINLP we develope an iterative algorithm which solves MIP relaxations that are adaptively refined. We are able to give convergence results for a wide range of MINLPs requiring only continuous nonlinearities with bounded domains and an oracle computing maxima of the nonlinearities on their domain. Moreover, the practicalness of our approach is shown numerically by an application from the field of gas network optimization.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Author:Robert Burlacu, Björn Geißler, Lars Schewe
Document Type:Preprint
Date of Publication (online):2017/07/21
Date of first Publication:2017/07/21
Release Date:2017/07/21
Tag:Mixed-Integer Nonlinear Programming; Piecewise Linearization
Institutes:Friedrich-Alexander-Universität Erlangen-Nürnberg