Specify that the *SYMPHONY*
software -- using the Rsymphony package --
should be used to solve a conservation planning problem
(Ralphs & Güzelsoy 2005).
This function can also be used to customize the behavior of the solver.
It requires the Rsymphony package to be installed.

```
add_rsymphony_solver(
x,
gap = 0.1,
time_limit = .Machine$integer.max,
first_feasible = FALSE,
verbose = TRUE
)
```

- x
`problem()`

object.- gap
`numeric`

gap to optimality. This gap is relative and expresses the acceptable deviance from the optimal objective. For example, a value of 0.01 will result in the solver stopping when it has found a solution within 1% of optimality. Additionally, a value of 0 will result in the solver stopping when it has found an optimal solution. The default value is 0.1 (i.e., 10% from optimality).- time_limit
`numeric`

time limit (seconds) for generating solutions. The solver will return the current best solution when this time limit is exceeded. The default value is the largest integer value (i.e.,`.Machine$integer.max`

), effectively meaning that solver will keep running until a solution within the optimality gap is found.- first_feasible
`logical`

should the first feasible solution be be returned? If`first_feasible`

is set to`TRUE`

, the solver will return the first solution it encounters that meets all the constraints, regardless of solution quality. Note that the first feasible solution is not an arbitrary solution, rather it is derived from the relaxed solution, and is therefore often reasonably close to optimality. Defaults to`FALSE`

.- verbose
`logical`

should information be printed while solving optimization problems? Defaults to`TRUE`

.

An updated `problem()`

object with the solver added to it.

*SYMPHONY* is an
open-source mixed integer programming solver that is part of the
Computational
Infrastructure for Operations Research (COIN-OR) project.
The Rsymphony package
provides an interface to COIN-OR and -- unlike dependencies for other
solvers -- is available on *CRAN*.
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.

Ralphs TK and Güzelsoy M (2005) The SYMPHONY callable library for mixed integer programming. In The Next Wave in Computing, Optimization, and Decision Technologies (pp. 61--76). Springer, Boston, MA.

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 solvers for an overview of all functions for adding a solver.

Other solvers:
`add_cbc_solver()`

,
`add_cplex_solver()`

,
`add_default_solver()`

,
`add_gurobi_solver()`

,
`add_highs_solver()`

,
`add_lsymphony_solver`

```
# \dontrun{
# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# create problem
p <-
problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions() %>%
add_rsymphony_solver(time_limit = 10, verbose = FALSE)
# generate solution
s <- solve(p)
# plot solution
plot(s, main = "solution", axes = FALSE)
# }
```