Compile a problem

Compile a conservation planning problem into an mixed integer linear programming problem.

Usage

compile(x, ...)

# S3 method for class 'ConservationProblem'
compile(x, compressed_formulation = NA, ...)

Arguments

x

problem() object.

...

not used.

compressed_formulation

logical value indicating if x should be compiled following the compressed formulation? If compressed_formulation = NA, then the compressed is used unless one of the constraints in x requires the expanded formulation. Defaults to NA.

Value

An optimization_problem() object.

Details

This function might be useful for those interested in understanding how their conservation planning problem() is expressed as a mathematical optimization problem. However, if x just needs to be solved, then the solve() function should be used directly.

Please note that in nearly all cases, the default value for compressed_formulation should be used. This is because manually setting the compressed_formulation will, at best, have no effect on the problem. At worst, it may result in an error, a mis-specified problem, or unnecessarily long solve times.

Examples

# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()

# build minimal conservation problem
p <-
  problem(sim_pu_raster, sim_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(0.1)

# compile the conservation problem into an optimization problem
o <- compile(p)

# print the optimization problem
print(o)
#> An optimization problem (<OptimizationProblem>)
#> • model sense: min
#> • dimensions:  5, 90, 450 (rows, columns, cells)
#> • variables:   90 (B)