The NEOS Server offers MINOS for the solution of nonlinearly
constrained optimization problems in
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
(say up to 1000).
For linear programs, MINOS uses a stable implementation
of the primal simplex method.
(Basis factors are maintained by LUSOL, a sparse LU package
with Markowitz factorizations and Bartels-Golub updating.)
For linearly constrained problems, a reduced-gradient method
is employed with quasi-Newton approximations to the reduced Hessian.
For nonlinear constraints, MINOS implements an SLC
(sequential linearly constrained) algorithm derived from
Robinson's method. Steplength control is heuristic
(for want of a suitable merit function), but superlinear
convergence is often achieved.
Additional information on MINOS can be found in the
AMPL/MINOS User's Guide.
MINOS was developed by
Bruce A. Murtagh
Using the NEOS Server for MINOS
The user must submit a model in
format to solve a nonlinearly constrained optimization problem.
Examples of models in AMPL format can be found in the
The model is specified by a model file, and optionally,
a data file and a commands file.
If the command file is specified it must contain
the AMPL solve command.
The commands file can contain any AMPL command
options for MINOS
with, for example,
option minos_options "timing=3 outlev=2";
Printing directed to standard out is returned
to the user with the output.
This NEOS solver executes on a Sun workstation.
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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)
Dry run: generate job XML instead of submitting it to NEOS
Please do not click the 'Submit to NEOS' button more than once.