The NEOS Server offers MINLP for the solution of mixedinteger
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 branchandbound 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 mixedinteger nonlinearly constrained optimization problem.
Examples of models in AMPL format can be found in the
MacMINLP
collection.
The mixedinteger 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:
Comments:
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
Please do not click the 'Submit to NEOS' button more than once.
