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The NEOS Server offers MINLP for the solution of mixed-integer nonlinearly constrained optimization problems in AMPL format. MINLP is suitable for large, nonlinearly constrained problems with a modest number of degrees of freedom.

MINLP implements a branch-and-bound algorithm, searching a tree whose nodes correpond to continuous nonlinearly constrained optimization problems. The continuous problems are solved using filterSQP, a Sequential Quadratic Programming solver that is suitable for solving large, nonlinearly constrained problems. Additional information on MINLP and filterSQP can be found here.

MINLP was developed by Roger Fletcher and Sven Leyffer.

Using the NEOS Server for MINLP

The user must submit a model in AMPL format to solve a mixed-integer nonlinearly constrained optimization problem. Examples of models in AMPL format can be found in the MacMINLP collection.

The mixed-integer nonlinearly constrained optimization problem must be specified by a model file with the options of a data file and a commands file. If the commands file is specified, it must contain the AMPL solve command. However, the commands file must not contain the model or data commands. The model and data files are renamed internally by NEOS.

The commands file may include AMPL commands and option settings for MINLP. To specify solver options, add

option minlp_options "OPTIONS";
where "OPTIONS" is a list of one or more of the MINLP options. See the MINLP Directives for AMPL. Printing directed to standard output is returned to the user with the output.

Enter the location of the AMPL model (local file)
Model File:

Enter the location of the AMPL data file (local file)
Data File:

Enter the location of the AMPL commands file (local file)
Commands File:


Dry run: generate job XML instead of submitting it to NEOS
Short Priority: submit to higher priority queue with maximum CPU time of 5 minutes
e-mail address:

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