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Add default solver

Source: R/add_default_solver.R
add_default_solver.Rd

Specify that the best solver currently available should be used to solve a conservation planning problem.

Usage

add_default_solver(x, ...)

Arguments

x

problem() or multi_problem() object.

...

arguments passed to the solver.

Value

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

Details

Ranked from best to worst, the available solvers that can be used are: add_gurobi_solver(), add_cplex_solver(), add_cbc_solver(), add_highs_solver(), add_lpsymphony_solver(), and finally add_rsymphony_solver(). For information on the performance of different solvers, please see Schuster et al. (2020).

References

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 also

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

Other functions for adding solvers: add_cbc_solver(), add_cplex_solver(), add_gurobi_solver(), add_highs_solver(), add_lsymphony_solver, add_rsymphony_solver()

Examples

# set seed for reproducibility
set.seed(600)

# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()

# create minimal problem with default portfolio
p <-
  problem(sim_pu_raster, sim_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(0.05) %>%
  add_binary_decisions() %>%
  add_default_solver(gap = 0, verbose = FALSE)

# solve problem
s <- solve(p)

# plot solution
plot(s)