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.

**Nonlinear objective functions and linear constraints**: A reduced-gradient algorithm is used. This is an active-set method (a natural extension of the simplex method). The variables are classified as basic, superbasic, and nonbasic, with the number of superbasics indicating the effective nonlinearity of the objective. The constraints are satisfied before the objective is evaluated. Feasibility is maintained thereafter. Search directions are generated using a quasi-Newton approximation to the reduced Hessian.**Nonlinear constraints**: A projected augmented Lagrangian algorithm is used. As in Robinson's method, each major iteration solves a linearly constrained subproblem to generate a search direction. The subproblem objective is an augmented Lagrangian function. The subproblem constraints are the true linear constraints plus linearizations of the nonlinear constraints. Convergence is usually achieved, although the steplength choice is heuristic.

MINOS was developed by Bruce A. Murtagh and Michael Saunders.

Further details on MINOS can be found in the MINOS User's
Manual.

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';where OPTIONS is a list of one or more of the available solver options for AMPL.

Web Submission Form

Enter the location of the AMPL model (local file)

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

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

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