ROI: An extensible R Optimization Infrastructure

Theußl, Stefan and Schwendinger, Florian and Hornik, Kurt ORCID: (2019) ROI: An extensible R Optimization Infrastructure. Research Report Series / Department of Statistics and Mathematics, 133. WU Vienna University of Economics and Business, Vienna.


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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 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: Paper
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
Depositing User: Florian Schwendinger
Date Deposited: 14 Nov 2017 09:54
Last Modified: 24 Oct 2019 13:43


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