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
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.
MINLP was developed by
and Sven Leyffer.
Additional information on MINLP and filterSQP can be found
The user must submit a model in AMPL
format. Examples are provided in the examples
section of the AMPL website.
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
option minlp_options 'OPTIONS';