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MINLP

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 which is suitable for solving large nonlinearly constrained problems. Additional information on MINLP can be found in the user manual for MINLP . Additional information on filterSQP can be found in the user manual for filterSQP .

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 MINLP - AMPL library .

The model is specified by a model file, and optionally, a data file and a commands file. If the command file is specified it must contain the AMPL solve command.

The commands file can contain any AMPL command or set options for MINLP . Printing directed to standard out 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:



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Dry run: generate job XML instead of submitting it to NEOS
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