The NEOS Server offers MINOS for the solution of nonlinearly constrained optimization problems in GAMS 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 (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.
MINOS was developed by Bruce A. Murtagh and Michael Saunders.
An option file can be used to specify MINOS options. 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.
The NEOS Server initially 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 the modeling language for further information.
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. Model File:
<modelname>.optfile = 1 ;
optfile = 1
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