knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of optplot
is provide functions to plot optimization problems/models such as mixed-integer linear programs. Work in progress. Contributions welcome.
You can install the released version of optplot from CRAN with:
install.packages("optplot")
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("dirkschumacher/optplot")
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.
# 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 $c$.
mat <- ompr::extract_constraints(model) A <- mat$matrix b <- mat$rhs cv <- ompr::objective_function(model)$solution
optplot::milp_plot(A, b, cv)
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