The NEOS Server offers MINLP for the solution of mixed-integer nonlinearly
constrained optimization problems. Problems can be submitted to MINLP on
NEOS in AMPL format.

The software package MINLP solves mixed integer nonlinear programming
(MINLP) problems by branch-and-bound. The solver guarantees finding global
solutions if the problem is convex. MINLP is also effective for solving
non-convex MINLP problems. Even though no guarantee can be given that a
global solution is found in this case, the solver is more robust than outer
approximation or Benders Decomposition, which usually cut away large parts
of the feasible region. MINLP can also be used to solve problems with
discrete variables.

MINLP implements a branch-and-bound scheme searching a tree whose nodes
correpond to continuous nonlinearly constrained optimization problems. The
resulting nonlinear programming (NLP) relaxations are solved using
filterSQP. The user can influence the branching decision by supplying
priorities for the integer variables. By default, the solver branches on the
variable with the highest priority first. If there is a tie, then the
variable with the largest fractional part is selected for for branching.

The problem must be specified in a model file. A data file and commands
files may also be provided. If the commands file is specified, it must
contain the AMPL solve command; however, it must not contain
the model or data commands. The model and data
files are renamed internally by NEOS.

The commands file may include option settings for the solver. To specify
solver options, add

Enter the location of the AMPL data file (local file)

Commands File

Enter the location of the AMPL commands file (local file)

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Additional Settings

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
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