ROI: An extensible R optimization infrastructure.

Stefan Theußl, Florian Schwendinger, Kurt Hornik

Publication: Scientific journalJournal articlepeer-review

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Optimization plays an important role in many methods routinely used in statistics, machine learning and data science. Often, implementations of these methods rely on highly specialized optimization algorithms, designed to be only applicable within a specific application. However, in many instances recent advances, in particular in the field of convex optimization, make it possible to conveniently and straightforwardly use modern solvers instead with the advantage of enabling broader usage scenarios and thus promoting reusability. This paper introduces the R optimization infrastructure ROI which provides an extensible infrastructure to model linear, quadratic, conic and general nonlinear optimization problems in a consistent way. Furthermore, the infrastructure administers many different solvers, reformulations, problem collections and functions to read and write optimization problems in various formats.
Original languageEnglish
Pages (from-to)1 - 64
JournalJournal of Statistical Software
Issue number15
Publication statusPublished - 2020

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 101015 Operations research
  • 101018 Statistics
  • 101019 Stochastics
  • 502009 Corporate finance
  • 101

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