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 GAMS format to solve an optimization problem. For security purposes, the model submitted must adhere to the following conventions:

- It must be self contained, i.e., no $include or $batinclude statements.
- It may not execute external programs, i.e., no $call or execute statements.
- No file creation, i.e. no put files or $echo except for a file named 'results.txt'. Other files created in the GAMS model file will be deleted.

If you are unfamiliar with GAMS, the GAMS Documentation includes a GAMS Tutorial and User's Guide. Examples of models in GAMS format can be found in the GAMS model library.

By default, the NEOS Server limits the amount of output generated in the listing file by turning off the symbol and unique element list, symbol cross references, and restricting the rows and columns listed to zero. This behavior can be changed by specifying the appropriate options in the model file. See the documentation on GAMS output for further information.

You may optionally submit an options file if you wish to override the default parameter settings for the solver.
Currently, the NEOS Server can only use **optfile=1** with GAMS input. Therefore, any model that specifies a different options file will not work as intended.

Web Submission Form

You can submit an optimization problem specified in the GAMS
modeling language to be solved using the optimization tools on
the NEOS Server. You need to specify the absolute path to a
GAMS file on your system.

The Options file is optional and requires that the
statement
exist before the solve statement in your model file. At this
time, only is supported; however,
options may also be specified within the model input file (see
the GAMS documentation).

`<modelname>.optfile = 1 ;`

`optfile = 1`

Optional GDX file for inputs. This file will be renamed to in.gdx. The model must include "$GDXIN in.gdx" to load this file

The solver will return a compressed GDX file which contains all symbols in the model.

The log file contains information generated by the algorithm
during a solve. Checking the box will cause the log file to be
included in the output returned.

Enter any additional comments here (e.g. to identify the data
for your own information). These comments will be returned
with your results.

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