Specify the software and configuration used to solve a conservation planning
problem. By default, the best available
software currently installed on the system will be used.
For information on the performance of different solvers,
please see Schuster *et al.* (2020) for benchmarks comparing the
run time and solution quality of some of these solvers when applied to
different sized datasets.

## Details

The following solvers can be used to find solutions for a
conservation planning `problem()`

:

`add_default_solver()`

This solver uses the best software currently installed on the system.

`add_gurobi_solver()`

*Gurobi*is a state-of-the-art commercial optimization software with an R package interface. We recommend using this solver if at all possible. 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.`add_cplex_solver()`

*IBM CPLEX*is a commercial optimization software. It is faster than the open source solvers available for generating prioritizations, however, it is not freely available. Similar to the*Gurobi*software, licenses are available to academics at no cost. This solver uses the cplexAPI package to solve problems using*IBM CPLEX*.`add_cbc_solver()`

*CBC*is an open-source mixed integer programming solver that is part of the Computational Infrastructure for Operations Research (COIN-OR) project. Preliminary benchmarks indicate that it is the fastest open source solver currently supported. We recommend using this solver if both*Gurobi*and*IBM CPLEX*are unavailable. It requires the rcbc package, which is currently only available on GitHub.`add_highs_solver()`

*HiGHS*is an open source optimization software. Although this solver can have comparable performance to the*CBC*solver for particular problems and is generally faster than the*SYMPHONY*based solvers (see below), it sometimes can take much longer than the*CBC*solver for particular problems.`add_lpsymphony_solver()`

*SYMPHONY*is an open-source mixed integer programming solver that is also part of the COIN-OR project. Although both*SYMPHONY*and*CBC*are part of the COIN-OR project, they are different software. The lpsymphony package provides an interface to the*SYMPHONY*software, and is distributed through Bioconductor. We recommend using this solver if the*CBC*and*HiGHS*solvers cannot be installed. This solver can use parallel processing to solve problems, so it is faster than Rsymphony package interface (see below).`add_rsymphony_solver()`

This solver provides an alternative interface to the

*SYMPHONY*solver using the Rsymphony package. Unlike other solvers, the Rsymphony package can be installed directly from the Comprehensive R Archive Network (CRAN). It is also the slowest of the available solvers.

## References

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.

## See also

Other overviews:
`constraints`

,
`decisions`

,
`importance`

,
`objectives`

,
`penalties`

,
`portfolios`

,
`summaries`

,
`targets`

## Examples

```
# \dontrun{
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# create basic problem
p <-
problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_proportion_decisions()
# create vector to store plot names
n <- c()
# create empty list to store solutions
s <- c()
# if gurobi is installed: create problem with added gurobi solver
if (require("gurobi")) {
p1 <- p %>% add_gurobi_solver(verbose = FALSE)
n <- c(n, "gurobi")
s <- c(s, solve(p1))
}
#> Loading required package: gurobi
#> Warning: package ‘gurobi’ was built under R version 4.4.0
#> Loading required package: slam
# if cplexAPI is installed: create problem with added CPLEX solver
if (require("cplexAPI")) {
p2 <- p %>% add_cplex_solver(verbose = FALSE)
n <- c(n, "CPLEX")
s <- c(s, solve(p2))
}
#> Loading required package: cplexAPI
# if rcbc is installed: create problem with added CBC solver
if (require("rcbc")) {
p3 <- p %>% add_cbc_solver(verbose = FALSE)
n <- c(n, "CBC")
s <- c(s, solve(p3))
}
#> Loading required package: rcbc
# if highs is installed: create problem with added HiGHs solver
if (require("highs")) {
p4 <- p %>% add_highs_solver(verbose = FALSE)
n <- c(n, "HiGHS")
s <- c(s, solve(p4))
}
#> Loading required package: highs
# create problem with added rsymphony solver
if (require("Rsymphony")) {
p5 <- p %>% add_rsymphony_solver(verbose = FALSE)
n <- c(n, "Rsymphony")
s <- c(s, solve(p5))
}
#> Loading required package: Rsymphony
# if lpsymphony is installed: create problem with added lpsymphony solver
if (require("lpsymphony")) {
p6 <- p %>% add_lpsymphony_solver(verbose = FALSE)
n <- c(n, "lpsymphony")
s <- c(s, solve(p6))
}
#> Loading required package: lpsymphony
# plot solutions
names(s) <- n
plot(terra::rast(s), axes = FALSE)
# }
```