Editing Stochastic programming (section)

===Modelling languages===
All discrete stochastic programming problems can be represented with any [[algebraic modeling language]], manually implementing explicit or implicit non-anticipativity to make sure the resulting model respects the structure of the information made available at each stage. 
An instance of an SP problem generated by a general modelling language tends to grow quite large (linearly in the number of scenarios), and its matrix loses the structure that is intrinsic to this class of problems, which could otherwise be exploited at solution time by specific decomposition algorithms.
Extensions to modelling languages specifically designed for SP are starting to appear, see:
*[[AIMMS]] – supports the definition of SP problems
*[[Extended Mathematical Programming (EMP)#EMP for Stochastic Programming|EMP SP]] (Extended Mathematical Programming for Stochastic Programming) – a module of [[General Algebraic Modeling System|GAMS]] created to facilitate stochastic programming (includes keywords for parametric distributions, chance constraints and risk measures such as [[Value at risk]] and [[Expected shortfall]]).
*[[SAMPL]] – a set of extensions to [[AMPL]] specifically designed to express stochastic programs (includes syntax for chance constraints, integrated chance constraints and [[robust optimization]] problems)
They both can generate SMPS instance level format, which conveys in a non-redundant form the structure of the problem to the solver.