Quotation Theußl, Stefan, Schwendinger, Florian, Hornik, Kurt. 2020. ROI: An extensible R optimization infrastructure. Journal of Statistical Software. 94 (15), 1-64.




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.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Statistical Software
Citation Index SCI
WU-Journal-Rating new FIN-A
Language English
Title ROI: An extensible R optimization infrastructure.
Volume 94
Number 15
Year 2020
Page from 1
Page to 64
Reviewed? Y
URL https://www.jstatsoft.org/article/view/v094i15
DOI http://dx.doi.org/10.18637/jss.v094.i15
Open Access N


Theußl, Stefan (Former researcher)
Schwendinger, Florian (Former researcher)
Hornik, Kurt (Details)
Institute for Statistics and Mathematics IN (Details)
Research Institute for Computational Methods FI (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1104 Applied mathematics (Details)
1105 Computer software (Details)
1121 Operations research (Details)
1162 Statistics (Details)
1165 Stochastics (Details)
5361 Financial management (Details)
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