Mathematical programming in R

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OMPR (Optimization Modelling Package) is a DSL to model and solve Mixed Integer Linear Programs. It is inspired by the excellent Jump project in Julia.

Here are some problems you could solve with this package:

• What is the cost minimal way to visit a set of clients and return home afterwards?
• What is the optimal conference time table subject to certain constraints (e.g. availability of a projector)?
• Sudokus
  

The Wikipedia article gives a good starting point if you would like to learn more about the topic.

This is a beta version. Currently working towards a first stable version for CRAN. At the moment not recommended for production systems / important analyses. Although most obvious bugs should be gone. Happy to get bug reports or feedback.

Supported problem classesObjective types

• Linear
  

Constraint types

• Linear
  

Variable types

• Continuous
• Integer-valued
  

Install

To install the current development version use devtools:

devtools::install_github("dirkschumacher/ompr")devtools::install_github("dirkschumacher/ompr.roi")

Available solver bindings[td]

Package	Description	Build Linux	Build Windows	Test coverage
ompr.roi	Bindings to ROI (GLPK, Symphony, CPLEX etc.)

A simple example:

1. library(dplyr)library(ROI)library(ROI.plugin.glpk)library(ompr)library(ompr.roi)result <- MIPModel() %>%  add_variable(x, type = "integer") %>%  add_variable(y, type = "continuous", lb = 0) %>%  set_bounds(x, lb = 0) %>%  set_objective(x + y, "max") %>%  add_constraint(x + y <= 11.25) %>%  solve_model(with_ROI(solver = "glpk"))get_solution(result, x)get_solution(result, y)

复制代码

API

These functions currently form the public API. More detailed docs can be found in the package function docs or on the website

DSL

• MIPModel() create an empty mixed integer linear model
• add_variable() adds variables to a model
• set_objective() sets the objective function of a model
• set_bounds()sets bounds of variables
• add_constraint() add constraints
• solve_model() solves a model with a given solver
• get_solution() returns the solution of a solved model for a given variable or group of variables
  

Solver

Solvers are in different packages. ompr.ROI uses the ROI package which offers support for all kinds of solvers.

• with_ROI(solver = "glpk") solve the model with GLPK. Install ROI.plugin.glpk
• with_ROI(solver = "symphony") solve the model with Symphony. Install ROI.plugin.symphony
• with_ROI(solver = "cplex") solve the model with CPLEX. Install ROI.plugin.cplex
• … See the ROI package for more plugins.
  

Further Examples

Please take a look at the docs for bigger examples.

Knapsacklibrary(dplyr)library(ROI)library(ROI.plugin.glpk)library(ompr)library(ompr.roi)max_capacity <- 5n <- 10weights <- runif(n, max = max_capacity)MIPModel() %>%  add_variable(x, i = 1:n, type = "binary") %>%  set_objective(sum_expr(weights * x, i = 1:n), "max") %>%  add_constraint(sum_expr(weights * x, i = 1:n) <= max_capacity) %>%  solve_model(with_ROI(solver = "glpk")) %>%   get_solution(x) %>%   filter(value > 0)

Bin Packing

An example of a more difficult model solved by symphony.

library(dplyr)library(ROI)library(ROI.plugin.symphony)library(ompr)library(ompr.roi)max_bins <- 10bin_size <- 3n <- 10weights <- runif(n, max = bin_size)MIPModel() %>%  add_variable(y, i = 1:max_bins, type = "binary") %>%  add_variable(x[i, j], i = 1:max_bins, j = 1:n, type = "binary") %>%  set_objective(sum_expr(y, i = 1:max_bins), "min") %>%  add_constraint(sum_expr(weights[j] * x[i, j], j = 1:n) <= y * bin_size, i = 1:max_bins) %>%  add_constraint(sum_expr(x[i, j], i = 1:max_bins) == 1, j = 1:n) %>%  solve_model(with_ROI(solver = "symphony", verbosity = 1)) %>%   get_solution(x[i, j]) %>%  filter(value > 0) %>%  arrange(i)


License

Currently GPL.

Contributing

As long as the package is under initial development please post an issue first before sending a PR.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms

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