The NEOS Server offers MINOS for the solution of nonlinearly constrained
optimization problems. Problems can be submitted to MINOS on the NEOS
server in AMPL or GAMS format.
MINOS is designed to solve "smooth" nonlinear programming (NLP)
problems. Smooth nonlinear functions can be accommodated in both the
objective and the constraints; nonlinear equation systems may also be solved
by omitting an objective. Nonsmooth nonlinearities are also accepted, but
MINOS is not designed for them and in general will not produce reliable
results when they are present. MINOS also offers a primal simplex method
for linear programming (LP) problems.
MINOS is suitable for large constrained problems with a linear or nonlinear
objective function and a mixture of linear and nonlinear constraints. It is
most efficient if the constraints are linear and there are not too many
degrees of freedom.
For linear programs, MINOS uses a stable implementation of the primal
simplex method. For linearly constrained problems, a reduced-gradient
method is employed with quasi-Newton approximations to the reduced Hessian.
For nonlinear constraints, MINOS implements a sequential linearly
constrained algorithm derived from Robinson's method. Step length control
is heuristic, but superlinear convergence is often achieved.
MINOS was developed by Bruce A. Murtagh and Michael Saunders.
Further details on MINOS can be found in the MINOS User's
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 minos_options 'OPTIONS';