https://github.com/dirkschumacher/optplot

An R package for plotting optimization problems/models
https://github.com/dirkschumacher/optplot

mixed-integer-programming plot r visualization

Last synced: 9 months ago
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An R package for plotting optimization problems/models

• Host: GitHub
• URL: https://github.com/dirkschumacher/optplot
• Owner: dirkschumacher
• License: other
• Created: 2018-05-03T16:43:14.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-05-03T19:26:55.000Z (over 7 years ago)
• Last Synced: 2025-03-10T15:11:30.975Z (9 months ago)
• Topics: mixed-integer-programming, plot, r, visualization
• Language: R
• Homepage:
• Size: 133 KB
• Stars: 4
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

          
---

output: github_document

---

```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# optplot

The goal of `optplot` is provide functions to plot optimization problems/models such as mixed-integer linear programs. Work in progress. Contributions welcome.

[![Travis build status](https://travis-ci.org/dirkschumacher/optplot.svg?branch=master)](https://travis-ci.org/dirkschumacher/optplot)

[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)

[![CRAN status](https://www.r-pkg.org/badges/version/optplot)](https://cran.r-project.org/package=optplot)

## Installation

You can install the released version of optplot from [CRAN](https://CRAN.R-project.org) with:

``` r

install.packages("optplot")

```

And the development version from [GitHub](https://github.com/) with:

``` r

# install.packages("devtools")

devtools::install_github("dirkschumacher/optplot")

```

## Example

This is a basic example which shows you how to plot the popular Travling Salesperson Problem. It uses the `ompr` package to model the MILP.

```{r example}

# based on the Miller–Tucker–Zemlin (MTZ) formulation

# More info here: https://www.unc.edu/~pataki/papers/teachtsp.pdf)

library(ompr)

library(magrittr)

set.seed(1234)

n <- 10

model <- MILPModel() %>%

  # we create a variable that is 1 iff we travel from city i to j

  add_variable(x[i, j], i = 1:n, j = 1:n, 

               type = "integer", lb = 0, ub = 1) %>%

  

  # a helper variable for the MTZ formulation of the tsp

  add_variable(u[i], i = 1:n, lb = 1, ub = n) %>% 

  

  # minimize travel distance

  set_objective(sum_expr(colwise(runif(n^2)) * x[i, j], i = 1:n, j = 1:n), "min") %>%

  

  # you cannot go to the same city

  set_bounds(x[i, i], ub = 0, i = 1:n) %>%

  

  # leave each city

  add_constraint(sum_expr(x[i, j], j = 1:n) == 1, i = 1:n) %>%

  #

  # visit each city

  add_constraint(sum_expr(x[i, j], i = 1:n) == 1, j = 1:n) %>%

  

  # ensure no subtours (arc constraints)

  add_constraint(u[i] >= 2, i = 2:n) %>% 

  add_constraint(u[i] - u[j] + 1 <= (n - 1) * (1 - x[i, j]), i = 2:n, j = 2:n)

```

Having defined the model, we can extract the constraint matrix $A$, the right hand side vector $b$ and the objective coefficent vector $c$.

```{r}

mat <- ompr::extract_constraints(model)

A <- mat$matrix

b <- mat$rhs

cv <- ompr::objective_function(model)$solution

```

```{r milp-plot}

optplot::milp_plot(A, b, cv)

```