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Minotaur: A Mixed-Integer Nonlinear Optimization Toolkit

Ashutosh Mahajan(amahajan***at***iitb.ac.in)
Sven Leyffer(leyffer***at***mcs.anl.gov)
Jeff Linderoth(linderoth***at***wisc.edu)
Jim Luedtke(jim.luedtke***at***wisc.edu)
Todd Munson(tmunson***at***mcs.anl.gov)

Abstract: We present a flexible framework for general mixed-integer nonlinear programming (MINLP), called Minotaur, that enables both algorithm exploration and structure exploitation without compromising computational efficiency. This paper documents the concepts and classes in our framework and shows that our implementations of standard MINLP techniques are efficient compared with other state-of-the-art solvers. We then describe structure-exploiting extensions that we implement in our framework and demonstrate their impact on solution times. Without a flexible framework that enables structure exploitation, finding global solutions to difficult nonconvex MINLP problems will remain out of reach for many applications.

Keywords: Mixed-Integer Nonlinear Programming, Global Optimization, Software

Category 1: Optimization Software and Modeling Systems

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 3: Global Optimization

Citation: Preprint ANL/MCS-P8010-0817, Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL., October 2017

Download: [PDF]

Entry Submitted: 10/16/2017
Entry Accepted: 10/16/2017
Entry Last Modified: 10/16/2017

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