Add a HiGHS solver

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.

Usage

add_highs_solver(
  x,
  gap = 0.1,
  time_limit = .Machine$integer.max,
  presolve = TRUE,
  threads = 1,
  start_solution = NULL,
  verbose = TRUE,
  control = list()
)

Arguments

x: problem() or multi_problem() object.
gap: numeric value denoting the optimality gap. This gap is relative and expresses the acceptable deviance from optimality. 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 value denoting the 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.
presolve: logical value indicating if the optimization problem should be simplified before solving it? Defaults to TRUE.
threads: integer value denoting the number of threads to use during optimization. Broadly speaking, we recommend setting threads to be no higher than the number of computational cores minus one or two (e.g., threads = parallel::detectCores(TRUE) - 2). This is because setting threads to be equal to the number of computational cores means that the solver and is fighting for resources with other software (e.g., Dropbox, iCloud, OneDrive, software updates, antivirus software, internet browsers) and, in turn, can result in computational bottlenecks that slow run times. Additionally, when setting threads to be a value greater than 1, we recommend checking memory (RAM) usage during the optimization process to ensure that the solver does not use up the majority of available memory. This is because solving optimization problems with multiple threads can involve creating multiple copies of the problem (e.g., threads = 5 may mean 5 copies) and exhausting most of the available memory will drastically slow run times. Defaults to 1.
start_solution: NULL or object containing the starting solution for the solver. This is can be useful because specifying a starting solution can speed up the optimization process. To specify a starting solution, start_solution should be in the same format as the planning units (i.e., a numeric, matrix, data.frame, terra::rast(), or sf::sf() object). See the Start solution format section for more information. Defaults to NULL such that no starting solution is used.
verbose: logical value indicating if information should be displayed during the optimization process. Defaults to TRUE.
control: list with additional parameters for tuning the optimization process. For example, control = list(simplex_strategy = 1) could be used to set the simplex_strategy parameter. See the online documentation for information on the parameters.

Value

An updated problem() or multi_problem() object with the solver added to it.

Details

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.

References

Huangfu Q and Hall JAJ (2018). Parallelizing the dual revised simplex method. Mathematical Programming Computation, 10: 119-142.

Examples

# 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)