ROI: The R Optimization Infrastructure Package

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 (non-reusable) optimization algorithms. However, in many instances recent advances, in particular in the field of convex optimization, make it possible to conveniently and straightforwardly use modern solvers (reusable) instead with the advantage of enabling broader usage scenarios and thus promoting reusability. This paper introduces the ROI (R Optimization Infrastructure) package 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.