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,

## Value

Object (i.e. ConservationProblem) with the solver added to it.

## Details

Gurobi is a state-of-the-art 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.

## Installation

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")


## Start solution format

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 units

The 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 units

The 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 units

The 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 units

The 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 units

The 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 units

The 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.

## References

Gurobi Optimization LLC (2021) Gurobi Optimizer Reference Manual. https://www.gurobi.com.

Schuster R, Hanson JO, Strimas-Mackey M, and Bennett JR (2020). Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems. PeerJ, 8: e9258.

## Examples

# \dontrun{
data(sim_pu_raster, sim_features)

# create problem
p <- problem(sim_pu_raster, sim_features) %>%
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) %>%