Save the mathematical formulation for a conservation planning problem()
to a file for mixed integer programming solvers. Note that this function
requires the Rsymphony package to be installed.
write_problem(x, path)problem() (i.e., ConservationProblem) object.
character file path to save the problem formulation.
The argument should contain a ".lp" or .mps" file extension
to specify whether the problem formulation will be saved in the
LP or
MPS
format (respectively).
Invisible TRUE indicating success.
# \dontrun{
# set seed for reproducibility
set.seed(500)
# load data
data(sim_pu_raster, sim_features)
# create minimal problem
p <- problem(sim_pu_sf[1:4, , drop = FALSE], sim_features,
cost_column = "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions()
# specify file path to save problem formulation
path <- file.path(tempdir(), "model.lp")
# save problem to file (using the Rsymphony package)
write_problem(p, path)
# print model file
cat(readLines(path), sep = "\n")
#> \Problem name:
#>
#> Minimize
#> obj: 215.863839903 x0 + 212.782348080 x1 + 207.496243710 x2 + 208.932169949 x3
#> Subject To
#> cons0: -0.277834862 x0 -0.277834862 x1 -0.277834862 x2 -0.277834862 x3 <= -0.277834862
#> cons1: -0.127301294 x0 -0.127301294 x1 -0.127301294 x2 -0.127301294 x3 <= -0.127301294
#> cons2: -0.316365081 x0 -0.316365081 x1 -0.316365081 x2 -0.316365081 x3 <= -0.316365081
#> cons3: -0.121097955 x0 -0.121097955 x1 -0.121097955 x2 -0.121097955 x3 <= -0.121097955
#> cons4: -0.176466230 x0 -0.176466230 x1 -0.176466230 x2 -0.176466230 x3 <= -0.176466230
#> Bounds
#> 0 <= x0 <= 1
#> 0 <= x1 <= 1
#> 0 <= x2 <= 1
#> 0 <= x3 <= 1
#> Integers
#> x0 x1 x2 x3
#> End
# }