Specify that the *SYMPHONY*
software -- using the lpsymphony 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 lpsymphony package to be installed
(see below for installation instructions).

```
add_lpsymphony_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.
This solver is provided because it may be easier to install
on some systems than the Rsymphony package. Additionally --
although the lpsymphony package doesn't provide the functionality
to specify the number of threads for solving a problem -- the
lpsymphony package will solve problems using parallel processing
(unlike the Rsymphony package). As a consequence, this
solver will likely generate solutions much faster than the
`add_rsymphony_solver()`

.
Although formal benchmarks examining the performance of this solver
have yet to be completed,
please see Schuster *et al.* (2020) for benchmarks comparing the
run time and solution quality of the Rsymphony solver.

The lpsymphony package is distributed through Bioconductor. To install the lpsymphony package, please use the following code:

```
if (!require(remotes)) install.packages("remotes")
::install_bioc("lpsymphony") remotes
```

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.

Other solvers:
`add_cbc_solver()`

,
`add_cplex_solver()`

,
`add_default_solver()`

,
`add_gurobi_solver()`

,
`add_highs_solver()`

,
`add_rsymphony_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(0.05) %>%
add_proportion_decisions() %>%
add_lpsymphony_solver(time_limit = 5, verbose = FALSE)
# generate solution
s <- solve(p)
# plot solution
plot(s, main = "solution", axes = FALSE)
# }
```