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$integer.max,
first_feasible = FALSE,
verbose = TRUE
)
problem()
object.
numeric
gap to optimality. This gap is relative
and expresses the acceptable deviance from the optimal objective.
For example, a value of 0.01 will result in the solver stopping when
it has found a solution within 1% of optimality.
Additionally, a value of 0 will result in the solver stopping
when it has found an optimal solution.
The default value is 0.1 (i.e., 10% from optimality).
numeric
time limit (seconds) for generating solutions.
The solver will return the current best solution when this time limit is
exceeded. The default value is the largest integer value
(i.e., .Machine$integer.max
), effectively meaning that solver
will keep running until a solution within the optimality gap is found.
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
.
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)
# }