MINOS
The NEOS Server offers MINOS for the solution of nonlinearly
constrained optimization problems in AMPL format. 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 reducedgradient method
is employed with quasiNewton 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.
Additional information on MINOS can be found in the
Using AMPL/MINOS Guide.
MINOS was developed by Bruce A. Murtagh and Michael Saunders.
Using the NEOS Server with MINOS
The user must submit a model in AMPL format to solve a nonlinear programming problem.
Examples of models in AMPL format can be found in the
netlib collection.
The nonlinear program is specified by a model file with the options
of a data file and a commands file. If the commands file is specified,
it must contain the AMPL solve command. The commands
file must not contain the model or data
commands. The model and data files are renamed internally by NEOS.
The commands file may include AMPL commands or set solver options for MINOS.
To specify solver options, add
option minos_options "OPTIONS";
where OPTIONS is a list of one or more of the MINOS options, which are
listed in the Using AMPL/MINOS Guide.
Enter the location of the AMPL model (local file)
Model File:
Enter the location of the AMPL data file (local file)
Data File:
Enter the location of the AMPL commands file (local file)
Commands File:
Comments:
Dry run: generate job XML instead of submitting it to NEOS
Short Priority: submit to higher priority queue with maximum CPU time of 5
minutes
Please do not click the 'Submit to NEOS' button more than once.
