rmpk is a lightweight package to model mixed integer linear programs. It is based on the API of the ompr package and is also inspired by the architecture of Julia JuMP.

The goal is to provide a modelling package that can both be used in packages and also in interactive analyses. It also has a different architecture as the modelling layer modifies a central solver. That solver could be an interface to ROI or a shared pointer to a specific solver. Thus giving the option to directly communicate with the solver while still using an algebraic modelling framework.

This is currently work in progress and experimental - but working. I might merge it with ompr but it could also become the successor of ompr … not sure yet.

If you want to see the package in action take a look at the articles in the docs.

Happy to receive feedback!

Still under development. Anything can change

Installation

You can install the released version of RMPK from CRAN with:

remotes::install_github("r-opt/rmpk")

Supported types

  • Linear Programming (LP)
  • Mixed Integer Linear Programming (MILP)
  • Mixed Integer Quadratic Programming (MIQP)
  • Mixed Integer Quadratically Constrained Programming (MIQCP)

Features

  • ✅ Algebraic modelling of mixed integer programming problems

  • ✅ Integer, binary and continious variables

  • ✅ Linear and quadratic constraints/objective

  • ✅ Bindings to most popular solvers through ROI

  • ✅ Support for character variable indexes

  • ✅ Access row/column duals of Linear Programs

  • ✅ Row generation through solver callbacks (e.g. for models with exponential many constraints)

  • 🚧 Variable and constraint names

  • 🚧 Initial feasible solutions

  • 🚧 Almost as fast as matrix code

Low Level ROI Example

library(rmpk)
library(ROI.plugin.glpk)
set.seed(42)
solver <- ROI_optimizer("glpk")
v <- rnorm(10)
w <- rnorm(10)
model <- optimization_model(solver)
x <- model$add_variable("x", type = "binary", i = 1:10)
model$set_objective(sum_expr(v[i] * x[i], i = 1:10), sense = "max")
model$add_constraint(sum_expr(w[i] * x[i], i = 1:10) <= 10)
model$optimize()
model$get_variable_value(x[i])
#>    name  i value
#> 1     x  1     1
#> 2     x  7     1
#> 3     x  5     1
#> 4     x  8     0
#> 5     x  2     0
#> 6     x 10     0
#> 7     x  9     1
#> 8     x  3     1
#> 9     x  6     0
#> 10    x  4     1

List of solvers

rmpk supports all solvers that implement the MOI interface. It also comes with a ROI_optimzer that internally uses ROI and thus gives access to most popular solvers out of the box. Note that ROI has its own plugin system and you need to install these solvers separately in addition to ROIoptimizer.

Solver Name R Package Github URL
alabama ROIoptimizer https://github.com/r-opt/ROIoptimizer
cbc ROIoptimizer https://github.com/r-opt/ROIoptimizer
cccp ROIoptimizer https://github.com/r-opt/ROIoptimizer
clp ROIoptimizer https://github.com/r-opt/ROIoptimizer
cplex ROIoptimizer https://github.com/r-opt/ROIoptimizer
deoptim ROIoptimizer https://github.com/r-opt/ROIoptimizer
ecos ROIoptimizer https://github.com/r-opt/ROIoptimizer
glpk ROIoptimizer https://github.com/r-opt/ROIoptimizer
glpk GLPKoptimizer https://github.com/r-opt/GLPKoptimizer
gurobi ROIoptimizer https://github.com/r-opt/ROIoptimizer
ipop ROIoptimizer https://github.com/r-opt/ROIoptimizer
lpsolve ROIoptimizer https://github.com/r-opt/ROIoptimizer
mosek ROIoptimizer https://github.com/r-opt/ROIoptimizer
msbinlp ROIoptimizer https://github.com/r-opt/ROIoptimizer
neos ROIoptimizer https://github.com/r-opt/ROIoptimizer
nloptr ROIoptimizer https://github.com/r-opt/ROIoptimizer
optimx ROIoptimizer https://github.com/r-opt/ROIoptimizer
qpoases ROIoptimizer https://github.com/r-opt/ROIoptimizer
quadprog ROIoptimizer https://github.com/r-opt/ROIoptimizer
scs ROIoptimizer https://github.com/r-opt/ROIoptimizer
symphony ROIoptimizer https://github.com/r-opt/ROIoptimizer

Contribute

The best way at the moment to contribute is to test the package, write documentation, propose features. Soon, code contributions are welcome as well.

Please note that the ‘rmpk’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

MIT

References and Inspiration

  • Dunning, Iain, Joey Huchette, and Miles Lubin. “JuMP: A modeling language for mathematical optimization.” SIAM Review 59.2 (2017): 295-320.
  • ompr
  • pulp