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Try AMPL!

You can run AMPL on a model and data file from the AMPL book by selecting one of each:

Book order:Alphabetical:


Or upload your own model and data files, if your browser supports it:
  Model:
    Data:
Or you can type or paste in models and data; push Submit without making a selection.

Now push or push to undo any changes you made.

Fine print:

This is an experimental service and we have disabled commands that affect security. Other than that, all the features of AMPL are available. Bear in mind that each time you push Send, it is a fresh run of AMPL and the solver; there is no memory from one run to the next except what is included in the “model and data” window.

The solvers are provided through the courtesy of their respective authors and owners, to whom we are deeply grateful. Solvers include:

  • BPMPD is a linear interior-point code by Csaba Mészáros; available from netlib.
  • DONLP2, a nonlinear solver by Peter Spellucci, is available from netlib.
  • Lancelot is a nonlinear solver by Andy Conn, Nick Gould and Philippe Toint.
  • LOQO is an interior-point solver by Robert Vanderbei. There are more AMPL nonlinear optimization examples on this page.
  • Lpsolve, a simplex code for LPs and MIPs by Michel Berkelaar, is based on lp_solve, available from the net.
  • MINOS is a linear and nonlinear solver by Bruce Murtagh and Michael Saunders; it is the default.
  • PATH is a complementarity solver by Michael Ferris, Steven Dirkse, and Todd Munson. Some example complementarity problems appear here.
  • SNOPT is a nonlinear solver by Philip Gill, Walter Murray, and Michael Saunders.
  • TN (by Stephen G. Nash) and LBFGSB (by Ciyou Zhu and Jorge Nocedal) are truncated Newton algorithms for bound-constrained problems — problems whose only constraints are bounds on the variables.

For more information on these and other AMPL solvers, check the AMPL solvers page.

Privacy policy:

We record your IP address and your interactions with this system, including any models and data that you provide, solely for the purpose of better understanding how to improve the service. We do not distribute this information to anyone else.

We’d like to hear from you if you have any problems or suggestions on how to make the interface more useful. Mail to info@ampl.com