StochasticPrograms.jl

A modeling framework for stochastic programming problems

Summary

StochasticPrograms models recourse problems where an initial decision is taken, unknown parameters are observed, followed by recourse decisions to correct any inaccuracy in the initial decision. The underlying optimization problems are formulated in JuMP.jl. In StochasticPrograms, model instantiation can be deferred until required. As a result, scenario data can be loaded/reloaded to create/rebuild the recourse model at a later stage, possibly on separate machines in a cluster. Another consequence of deferred model instantiation is that StochasticPrograms.jl can provide stochastic programming constructs, such as expected value of perfect information (EVPI) and value of the stochastic solution (VSS), to gain deeper insights about formulated recourse problems. A good introduction to recourse models, and to the stochastic programming constructs provided in this package, is given in Introduction to Stochastic Programming. A stochastic program has a structure that can be exploited in solver algorithms. Therefore, StochasticPrograms provides a structured solver interface, implemented by LShapedSolvers.jl and ProgressiveHedgingSolvers.jl. StochasticPrograms has parallel capabilities, implemented using the standard Julia library for distributed computing.

Features

Flexible problem definition
Deferred model instantiation
Scenario data injection
Natively distributed
Interface to structure-exploiting solver algorithms
Efficient parallel implementations of classical algorithms

Consider Quick start for a tutorial explaining how to get started using StochasticPrograms.

Some examples of models written in StochasticPrograms can be found on the Examples page.

See the Index for the complete list of documented functions and types.

Manual Outline

Quick start

Stochastic data

Examples

Farmer problem

Library Outline

Public interface

Solver interface