The NEOS Server offers MINLP for the solution of mixed-integer
nonlinearly constrained optimization problems in
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
Additional information on MINLP and filterSQP can be found
MINLP was developed by
and Sven Leyffer.
Using the NEOS Server for MINLP
The user must submit a model in
format to solve a mixed-integer nonlinearly constrained optimization problem.
Examples of models in AMPL format can be found in the
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
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
Printing directed to standard output is returned
to the user with the output.
Enter the location of the AMPL model (local file)
Enter the location of the AMPL data file (local file)
Enter the location of the AMPL commands file (local 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
Please do not click the 'Submit to NEOS' button more than once.