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.
MINLP was developed by Roger Fletcher and Sven Leyffer. Additional information on MINLP and filterSQP can be found here.
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
solve
model
data
option minlp_options 'OPTIONS';