sdpt3r: Semidefinite Quadratic Linear Programming in R

Authors

Adam Rahman, University of Waterloo

Date of this Version

12-2018

Document Type

Article

Citation

The R Journal (December 2018) 10(2); Editor: John Verzani

Comments

Copyright 2018, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

We present the package sdpt3r, an R implementation of the Matlab package SDPT3 (Toh et al., 1999). The purpose of the software is to solve semidefinite quadratic linear programming (SQLP) problems, which encompasses problems such as D-optimal experimental design, the nearest correlation matrix problem, and distance weighted discrimination, as well as problems in graph theory such as finding the maximum cut or Lovasz number of a graph.

Current optimization packages in R include Rdsdp, Rcsdp, scs, cccp, and Rmosek. Of these, scs and Rmosek solve a similar suite of problems. In addition to these solvers, the R packages CXVR and ROI provide sophisticated modelling interfaces to these solvers. As a point of difference from the current solvers in R, sdpt3r allows for log-barrier terms in the objective function, which allows for problems such as the D-optimal design of experiments to be solved with minimal modifications. The sdpt3r package also provides helper functions, which formulate the required input for several well-known problems, an additional perk not present in the other R packages.