Specify that the Gurobi software
(Gurobi Optimization LLC 2021) should be used to solve a
conservation planning problem()
. This function can also be used to
customize the behavior of the solver.
It requires the gurobi package to be installed
(see below for installation instructions).
add_gurobi_solver( x, gap = 0.1, time_limit = .Machine$integer.max, presolve = 2, threads = 1, first_feasible = FALSE, numeric_focus = FALSE, node_file_start = Inf, start_solution = NULL, verbose = TRUE )
x 


gap 

time_limit 

presolve 

threads 

first_feasible 

numeric_focus 

node_file_start 

start_solution 

verbose 

Object (i.e. ConservationProblem
) with the solver
added to it.
Gurobi is a stateoftheart commercial optimization software with an R package interface. It is by far the fastest of the solvers available for generating prioritizations, however, it is not freely available. That said, licenses are available to academics at no cost. The gurobi package is distributed with the Gurobi software suite. This solver uses the gurobi package to solve problems. For information on the performance of different solvers, please see Schuster et al. (2020) for benchmarks comparing the run time and solution quality of different solvers when applied to different sized datasets.
Please see the Gurobi Installation Guide vignette for details on installing the Gurobi software and the gurobi package. You can access this vignette online or using the following code:
vignette("gurobi_installation", package = "prioritizr")
Broadly speaking, the argument to start_solution
must be in the same
format as the planning unit data in the argument to x
.
Further details on the correct format are listed separately
for each of the different planning unit data formats:
x
has numeric
planning unitsThe argument to start_solution
must be a
numeric
vector with each element corresponding to a different planning
unit. It should have the same number of planning units as those
in the argument to x
. Additionally, any planning units missing
cost (NA
) values should also have missing (NA
) values in the
argument to start_solution
.
x
has matrix
planning unitsThe argument to start_solution
must be a
matrix
vector with each row corresponding to a different planning
unit, and each column correspond to a different management zone.
It should have the same number of planning units and zones
as those in the argument to x
. Additionally, any planning units
missing cost (NA
) values for a particular zone should also have a
missing (NA
) values in the argument to start_solution
.
x
has Raster
planning unitsThe argument to start_solution
be a Raster
object where different grid cells (pixels) correspond
to different planning units and layers correspond to
a different management zones. It should have the same dimensionality
(rows, columns, layers), resolution, extent, and coordinate reference
system as the planning units in the argument to x
. Additionally,
any planning units missing cost (NA
) values for a particular zone
should also have missing (NA
) values in the argument to start_solution
.
x
has data.frame
planning unitsThe argument to start_solution
must
be a data.frame
with each column corresponding to a different zone,
each row corresponding to a different planning unit, and cell values
corresponding to the solution value. This means that if a data.frame
object containing the solution also contains additional columns, then
these columns will need to be subsetted prior to using this function
(see below for example with sf::sf()
data).
Additionally, any planning units missing cost
(NA
) values for a particular zone should also have missing (NA
)
values in the argument to start_solution
.
x
has Spatial
planning unitsThe argument to start_solution
must be a Spatial
object with each column corresponding to a
different zone, each row corresponding to a different planning unit, and
cell values corresponding to the solution value. This means that if the
Spatial
object containing the solution also contains additional
columns, then these columns will need to be subsetted prior to using this
function (see below for example with sf::sf()
data).
Additionally, the argument to start_solution
must also have the same
coordinate reference system as the planning unit data.
Furthermore, any planning units missing cost
(NA
) values for a particular zone should also have missing (NA
)
values in the argument to start_solution
.
x
has sf::sf()
planning unitsThe argument to start_solution
must be
a sf::sf()
object with each column corresponding to a different
zone, each row corresponding to a different planning unit, and cell values
corresponding to the solution value. This means that if the
sf::sf()
object containing the solution also contains additional
columns, then these columns will need to be subsetted prior to using this
function (see below for example).
Additionally, the argument to start_solution
must also have the same
coordinate reference system as the planning unit data.
Furthermore, any planning units missing cost
(NA
) values for a particular zone should also have missing (NA
)
values in the argument to start_solution
.
Gurobi Optimization LLC (2021) Gurobi Optimizer Reference Manual. https://www.gurobi.com.
Schuster R, Hanson JO, StrimasMackey M, and Bennett JR (2020). Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems. PeerJ, 8: e9258.
# \dontrun{ # load data data(sim_pu_raster, sim_features) # create problem p < problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_binary_decisions() %>% add_gurobi_solver(gap = 0, verbose = FALSE) # generate solution %>% s < solve(p) # plot solution plot(s, main = "solution", axes = FALSE, box = FALSE)# create a similar problem with boundary length penalties and # specify the solution from the previous run as a starting solution p2 < problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_boundary_penalties(10) %>% add_binary_decisions() %>% add_gurobi_solver(gap = 0, start_solution = s, verbose = FALSE) # generate solution s2 < solve(p2) # plot solution plot(s2, main = "solution with boundary penalties", axes = FALSE, box = FALSE)# }