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Efficient Stochastic Programming in Julia

Author

Listed:
  • Martin Biel

    (Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, Kungliga Tekniska Högskolan Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

  • Mikael Johansson

    (Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, Kungliga Tekniska Högskolan Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

Abstract

We present StochasticPrograms.jl , a user-friendly and powerful open-source framework for stochastic programming written in the Julia language. The framework includes both modeling tools and structure-exploiting optimization algorithms. Stochastic programming models can be efficiently formulated using an expressive syntax, and models can be instantiated, inspected, and analyzed interactively. The framework scales seamlessly to distributed environments. Small instances of a model can be run locally to ensure correctness, whereas larger instances are automatically distributed in a memory-efficient way onto supercomputers or clouds and solved using parallel optimization algorithms. These structure-exploiting solvers are based on variations of the classical L-shaped, progressive-hedging, and quasi-gradient algorithms. We provide a concise mathematical background for the various tools and constructs available in the framework along with code listings exemplifying their usage. Both software innovations related to the implementation of the framework and algorithmic innovations related to the structured solvers are highlighted. We conclude by demonstrating strong scaling properties of the distributed algorithms on numerical benchmarks in a multinode setup. Summary of Contribution: This paper presents StochasticPrograms.jl, an open-source framework for stochastic programming implemented in the Julia programming language. The framework includes an expressive syntax for formulating stochastic programming models as well as versatile analysis tools and parallel optimization algorithms. The framework will prove useful to researchers, educators, and industrial users alike. Researchers will benefit from the readily extensible open-source framework, in which they can formulate complex stochastic models or quickly typeset and test novel optimization algorithms. Educators of stochastic programming will benefit from the clean and expressive syntax. Moreover, the framework supports analysis tools and stochastic programming constructs from classical theory and leading textbooks. We strongly believe that the StochasticPrograms.jl framework can reduce the barrier to entry for incoming practitioners of stochastic programming. Industrial practitioners can make use of StochasticPrograms.jl to rapidly formulate complex models, analyze small instances locally, and then run large-scale instances in production. In doing so, they get distributed capabilities for free without changing the code and access to well-tested state-of-the-art implementations of parallel structure-exploiting solvers. As the framework is open-source, anyone from these target audiences can contribute with new functionality to the framework. In conclusion, by providing both an intuitive interface for new users and an extensive development environment for expert users, StochasticPrograms.jl has strong potential to further the field of stochastic programming.

Suggested Citation

  • Martin Biel & Mikael Johansson, 2022. "Efficient Stochastic Programming in Julia," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 1885-1902, July.
    • Handle: RePEc:inm:orijoc:v:34:y:2022:i:4:p:1885-1902
    DOI: 10.1287/ijoc.2022.1158
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    References listed on IDEAS

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    Cited by:

    1. Xiaotie Chen & David L. Woodruff, 2023. "Software for Data-Based Stochastic Programming Using Bootstrap Estimation," INFORMS Journal on Computing, INFORMS, vol. 35(6), pages 1218-1224, November.

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