AMPL - Features

Features

AMPL features a mixture of declarative and imperative programming styles. Formulation of optimization models takes place through declarative language elements such as sets, scalar and multidimensional parameters, decision variables, objectives and constraints, which allow for a concise description of most problems in the domain of mathematical optimization.

Procedures and control flow statements are available in AMPL for

  • the exchange of data with external data sources such as spreadsheets, databases, XML and text files
  • data pre- and post-processing tasks around optimization models
  • the construction of hybrid algorithms for problem types for which no direct efficient solvers are available.

To support re-use and simplify construction of large-scale optimization problems, AMPL allows separation of model and data.

AMPL supports a wide range of problem types, among them:

  • Linear programming
  • Quadratic programming
  • Nonlinear programming
  • Mixed-integer programming
  • Mixed-integer quadratic programming with or without convex quadratic constraints
  • Mixed-integer nonlinear programming
  • Second-order cone programming
  • Global optimization
  • Semidefinite programming problems with bilinear matrix inequalities
  • Complementarity problems (MPECs) in discrete or continuous variables
  • Constraint programming

AMPL invokes a solver in a separate process which has the following advantages:

  • Solver crashes do not affect the interpreter
  • 32-bit version of AMPL can be used with a 64-bit solver and vice versa

Interaction with the solver is done through a well-defined nl interface.

Read more about this topic:  AMPL

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