A Library of Mixed-Integer and Continuous Nonlinear Programming Instances
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This website hosts a collection of problem instances from the diverse classes of mixed-integer nonlinear programming (MINLP) and continuous nonlinear programming (NLP).
Since 2001, the Mixed-Integer Nonlinear Programming Library (MINLPLib) and the Nonlinear Programming Library (GLOBALLib) have provided algorithm developers with a large and varied set of both theoretical and practical test models. In recent years, major updates to MINLPLib lead to the inclusion of more instances, more solution points, and more information on each instance. Additionally, also purely continuous instances have been added to MINLPLib, including those from GLOBALLib. The original MINLPLib is still available here and GLOBALLib is available here.
The primary format for all instances is the GAMS scalar format. Additionally, instances are provided in a number of other formats, as far as conversion is possible.
Maintainer: Stefan Vigerske, svigerske at gams.com
Call for Instances
We are looking for more interesting and challenging (MI)NLPs from all fields of Operations Research and Combinatorial Optimization, ideally those which have been built to model real life problems.
If you would like to contribute, please send your instances by e-mail. We accept any well-known format that can be translated into GAMS. This includes AMPL (preferrably .nl), GAMS, ZIMPL, BARON, CPLEX LP, MPS, PIP, and OSiL.
Further, if you have a MINLP model that you would like to discuss with other people, be reminded of the minlp.org initiative. We are monitoring minlp.org and add model instantiations from minlp.org to MINLPLib occassionally.
- What are the default bounds on variables in a GAMS model?
See the GAMS documentation. However, MINLPLib works around the default upper bound of 100 for integer variables by setting the option
intvarup=0(see, e.g., model jit1). Therefore, for integer variables a default lower bound of 0 and a default upper bound of +infinity is assumed.
- Do the reported solution points and dual bounds allow to rank the performance of solvers?
No. New solutions are usually only added when improving the primal bound. If several solvers find the same solution, it is random which solver is attributed to that solution. Further, the reported dual bounds are just the best value as computed in some run with some option settings on some machine at some time in the past.