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
which is suitable for solving large nonlinearly constrained
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
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
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
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)
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.