Execute preliminary calculations in a conservation problem and store the results for later use. This function is useful when creating slightly different versions of the same conservation planning problem that involve the same pre-processing steps (e.g., calculating boundary data), because means that the same calculations will not be run multiple times.
Arguments
- x
problem()object.
Details
This function is used for the effect of modifying the input
ConservationProblem object. As such, it does not return
anything. To use this function with pipe() operators, use the
%T>% operator and not the %>% operator.
Examples
# \dontrun{
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# let us imagine a scenario where we wanted to understand the effect of
# setting different targets on our solution.
# create a conservation problem with no targets
p <-
problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_boundary_penalties(10, 0.5) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE)
# create a copies of p and add targets
p1 <- p %>% add_relative_targets(0.1)
p2 <- p %>% add_relative_targets(0.2)
p3 <- p %>% add_relative_targets(0.3)
# now solve each of the different problems and record the time spent
# solving them
s1 <- system.time({solve(p1); solve(p2); solve(p3)})
# This approach is inefficient. Since these problems all share the same
# planning units it is actually performing the same calculations three times.
# To avoid this, we can use the "run_calculations" function before creating
# the copies. Normally, R runs the calculations just before solving the
# problem
# recreate a conservation problem with no targets and tell R run the
# preliminary calculations. Note how we use the %T>% operator here.
p <-
problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_boundary_penalties(10, 0.5) %>%
add_binary_decisions() %>%
add_default_solver(verbose = FALSE) %T>%
run_calculations()
# create a copies of p and add targets just like before
p1 <- p %>% add_relative_targets(0.1)
p2 <- p %>% add_relative_targets(0.2)
p3 <- p %>% add_relative_targets(0.3)
# solve each of the different problems and record the time spent
# solving them
s2 <- system.time({solve(p1); solve(p2); solve(p3)})
# now lets compare the times
print(s1) # time spent without running preliminary calculations
#> user system elapsed
#> 0.926 0.000 0.944
print(s2) # time spent after running preliminary calculations
#> user system elapsed
#> 0.861 0.000 0.862
# As we can see, we can save time by running the preliminary
# calculations before making copies of the problem with slightly
# different constraints. Although the time saved in this example
# is rather small, this is because the example data are very small.
# We would expect larger time savings for larger datasets.
# }