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NEOS Interfaces to LGO

Sample Submissions
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The NEOS Server offers the LGO (Lipschitz-Continuous Global Optimizer) solver suite. Problems can be submitted to LGO in AMPL format on the NEOS server.

LGO is a global-local solver for constrained nonlinear optimization, assuming continuous model structure. LGO is particularly suitable to analyze design and operational decisions related to complete stand-alone 'black box' systems, and to models that are based on limited, difficult-to-use, confidential, or (due to ongoing development) often changing analytical information. LGO is recommended for solving highly nonlinear problems, with the number of constraints and variables not exceeding a few thousand. Run times can become substantial for larger problems due to the global search feature, but LGO can also be used as a local solver.

LGO integrates a suite of robust and efficient global and local scope optimization strategies. These include the following component algorithms:


LGO is maintained by Pintér Consulting Services, Inc. Further information on LGO can be found on the LGO Website.

Using the NEOS Server for LGO/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 lgo_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)
Additional Settings

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