SDDP.jl: A Julia Package for Stochastic Dual Dynamic Programming

Oscar Dowson () and Lea Kapelevich ()
Additional contact information
Oscar Dowson: Department of Industrial Engineering and Management Sciences, McCormick School of Engineering, Northwestern University, Evanston, Illinois 60208
Lea Kapelevich: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

INFORMS Journal on Computing, 2021, vol. 33, issue 1, 27-33

Abstract: We present SDDP.jl , an open-source library for solving multistage stochastic programming problems using the stochastic dual dynamic programming algorithm. SDDP.jl is built on JuMP, an algebraic modeling language in Julia. JuMP provides SDDP.jl with a solver-agnostic, user-friendly interface. In addition, we leverage unique features of Julia, such as multiple dispatch, to provide an extensible framework for practitioners to build on our work. SDDP.jl is well tested, and accessible documentation is available at https://github.com/odow/SDDP.jl .

Keywords: Julia; JuMP; stochastic dual dynamic programming (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
https://doi.org/10.1287/ijoc.2020.0987 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:1:p:27-33

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().