ROI: An extensible R optimization infrastructure.

Stefan Theußl, Florian Schwendinger, Kurt Hornik

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

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Abstract

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.
OriginalspracheEnglisch
Seiten (von - bis)1 - 64
FachzeitschriftJournal of Statistical Software
Jahrgang94
Ausgabenummer15
DOIs
PublikationsstatusVeröffentlicht - 2020

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

  • 102022 Softwareentwicklung
  • 101015 Operations Research
  • 101018 Statistik
  • 101019 Stochastik
  • 502009 Finanzwirtschaft
  • 101 not use (Altbestand)

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