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SDDP.jl

Warn

SDDP.jl under went a major re-write to be compatible with JuMP v0.19 and Julia v1.0. The Upgrading guide has advice on how to upgrade your existing SDDP.jl models.

SDDP.jl is a package for solving large multistage convex stochastic programming problems using stochastic dual dynamic programming. In this manual, we're going to assume a reasonable amount of background knowledge about stochastic optimization, the SDDP algorithm, Julia, and JuMP.

Info

If you haven't used JuMP before, we recommend that you read the JuMP documentation and try building and solving JuMP models before trying SDDP.jl.

Installation

You can install SDDP.jl as follows:

import Pkg
Pkg.add("https://github.com/odow/SDDP.jl.git")

Tutorials

Once you've got SDDP.jl installed, you should read some tutorials, beginning with Basic I: first steps.

Citing SDDP.jl

If you use SDDP.jl, we ask that you please cite the following paper:

@article{dowson_sddp.jl,
	title = {{SDDP}.jl: a {Julia} package for stochastic dual dynamic programming},
	url = {http://www.optimization-online.org/DB_HTML/2017/12/6388.html},
	journal = {Optimization Online},
	author = {Dowson, Oscar and Kapelevich, Lea},
	year = {2017}
}

If you use the infinite horizon functionality, we ask that you please cite the following paper:

@article{dowson_policy_graph,
	title = {The policy graph decomposition of multistage stochastic
      optimization problems},
	url = {http://www.optimization-online.org/DB_HTML/2018/11/6914.html},
	journal = {Optimization Online},
	author = {Dowson, Oscar},
	year = {2018}
}