Keywords: optimization, mathematical programming
Webpages:
https://cran.r-project.org/web/packages/ROI/index.html,
https://r-forge.r-project.org/projects/roi/ Optimization plays an increasingly important role in statistical computing. Typical applications include, among others, various types of regression, classification and low rank matrix approximations. Due to its wide application there exist many resources concerned with optimization. These resources involve software for modeling, solving and randomly generating optimization problems, as well as optimization problem collections used to benchmark optimization solvers. The
R Optimization Infrastructure package
ROI bundles many of the available resources used in optimization into a unified framework. It constitutes a unified way to formulate and store optimization problems by utilizing the rich language features
R has to offer, rather than creating a new language. In
ROI an optimization problem is stored as a single object, which ensures that it can be easily be saved and exchanged. Furthermore, the streamlined construction of optimization problems combined with a sophisticated plugin structure allows package authors and users to exploit different solver options by just changing the solver name. Currently, the
ROI plugins include solvers for general purpose nonlinear optimization as well as for linear, quadratic and conic programming. Additionally, plugins for reading and writing optimization problems in various formats (e.g. MPS, LP) and plugins for problem collections (e.g. netlib, miplib) transformed into the
ROI format are available.
