Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Stefan Theußl, Florian Schwendinger, Kurt Hornik
Title: ROI: An Extensible R Optimization Infrastructure
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.

Page views:: 1660. Submitted: 2017-10-22. Published: 2020-09-02.
Paper: ROI: An Extensible R Optimization Infrastructure     Download PDF (Downloads: 1022)
ROI_1.0-0.tar.gz: R source package Download (Downloads: 68; 108KB)
v94i15.R: R replication code Download (Downloads: 54; 17KB)
v94i15-supplements.R: R replication code, supplements Download (Downloads: 53; 7KB)

DOI: 10.18637/jss.v094.i15

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.