NEOS Interfaces to CONOPT

Sample Submissions
WWW Form - XML-RPC

CONOPT

The NEOS Server offers CONOPT for the solution of nonlinearly constrained optimization problems. Problems can be submitted to CONOPT on the NEOS server in AMPL or GAMS format.

CONOPT is a solver for general nonlinear programming models with sparse nonlinear constraints. It is a feasible-path method based on the GRG (generalized reduced gradient) algorithm. CONOPT is well-suited to models with very nonlinear constraints. It is a fast method for finding an initial feasible solution that is particularly well-suited to models with few degrees of freedom. Also, for models where many equations can be solved one by one, CONOPT will take advantage of this property. Similarly, CONOPT eliminates from the model intermediate variables only used to define objective function terms and moves the constraints into the objective function.

CONOPT was developed and is currently maintained by ARKI Consulting and Development A/S in Bagsvaerdvej, Denmark. For more information, visit the CONOPT website.

Reference:


Using the NEOS Server for CONOPT/AMPL


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

Web Submission Form
Model File
Enter the location of the AMPL model (local file)
Data File
Enter the location of the AMPL data file (local file)
Commands File
Enter the location of the AMPL commands file (local file)
Comments
Additional Settings


E-Mail address:
Please do not click the 'Submit to NEOS' button more than once.