The NEOS Server offers MINTO for the solution of mixed-integer linear programs posed in AMPL format.
MINTO (Mixed INT Optimizer) is a mixed integer linear programming (MILP) solver that implements a branch-and-bound algorithm with linear programming relaxations. MINTO provides automatic constraint classification, preprocessing, primal heuristics, and constraint generation. It has built-in cut generation and can create knapsack cuts, GUB cuts, clique cuts, implication cuts, flow cuts, mixed integer rounding and Gomory cuts. The user can customize MINTO by implementing problem-specific application routines.
MINTO does not have its own linear programming (LP) solver. This version of MINTO uses the LP solver Clp, from the Coin-OR project to solve the relaxations.
The original authors of MINTO are: Martin Savelsbergh, Georgia Tech, and George Nemhauser, Georgia Tech. MINTO is currently maintained by Jeff Linderoth, University of Wisconsin, Madison. References:
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 minto_options 'OPTIONS';
The commands file can contain any AMPL command or set options for MINTO. by modifying the variable 'mintoamp_options'. See the MINTO-AMPL options page to get a listing of the various options.
A few more items of note specific to the MINTO-AMPL solver