ROI: An extensible R optimization infrastructure.

Theußl, Stefan and Schwendinger, Florian and Hornik, Kurt ORCID: (2020) ROI: An extensible R optimization infrastructure. Journal of Statistical Software, 94 (15). pp. 1-64. ISSN 1548-7660

Available under License Creative Commons: Attribution 4.0 International (CC BY 4.0).

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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.

Item Type: Article
Keywords: optimization, mathematical programming, linear programming, quadratic programming, convex programming, nonlinear programming, mixed integer programming, R
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Forschungsinstitute > Rechenintensive Methoden
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 23 Sep 2020 09:17
Last Modified: 01 Oct 2020 10:49
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