Specify that the HiGHS software should be used to solve a conservation planning problem (Huangfu and Hall 2018). This function can also be used to customize the behavior of the solver. It requires the highs package to be installed.
add_highs_solver(
x,
gap = 0.1,
time_limit = .Machine$integer.max,
presolve = TRUE,
threads = 1,
verbose = TRUE
)
problem()
object.
numeric
gap to optimality. This gap is relative
and expresses the acceptable deviance from the optimal objective.
For example, a value of 0.01 will result in the solver stopping when
it has found a solution within 1% of optimality.
Additionally, a value of 0 will result in the solver stopping
when it has found an optimal solution.
The default value is 0.1 (i.e., 10% from optimality).
numeric
time limit (seconds) for generating solutions.
The solver will return the current best solution when this time limit is
exceeded. The default value is the largest integer value
(i.e., .Machine$integer.max
), effectively meaning that solver
will keep running until a solution within the optimality gap is found.
logical
attempt to simplify the
problem before solving it? Defaults to TRUE
.
integer
number of threads to use for the
optimization algorithm. The default value is 1.
logical
should information be printed while solving
optimization problems? Defaults to TRUE
.
An updated problem()
object with the solver added to it.
HiGHS is an open source optimization software.
Although this solver can have comparable performance to the CBC solver
(i.e., add_cbc_solver()
) for particular problems and is generally faster
than the SYMPHONY based solvers (i.e., add_rsymphony_solver()
,
add_lpsymphony_solver()
), it can sometimes take much longer than the
CBC solver for particular problems. This solver is recommended if
the add_gurobi_solver()
, add_cplex_solver()
, add_cbc_solver()
cannot
be used.
Huangfu Q and Hall JAJ (2018). Parallelizing the dual revised simplex method. Mathematical Programming Computation, 10: 119-142.
Other solvers:
add_cbc_solver()
,
add_cplex_solver()
,
add_default_solver()
,
add_gurobi_solver()
,
add_lsymphony_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(0.1) %>%
add_binary_decisions() %>%
add_highs_solver(gap = 0, verbose = FALSE)
# generate solution
s <- solve(p)
# plot solution
plot(s, main = "solution", axes = FALSE)
# }