Mixed Integer Linear and Quadratic Programming in R
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
You can install the released version of RMPK from
CRAN with:
remotes::install_github("r-opt/rmpk")
✅ 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
…
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
rmpk supports all solvers that implement the MOI
interface. It also comes with aROI_optimzer that internally usesROI 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 toROIoptimizer.
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.
MIT