Specify the software and configuration used to solve a conservation planning problem(). By default, the best available software currently installed on the system will be used. For information on the performance of different solvers, please see Schuster et al. (2020) for benchmarks comparing the run time and solution quality of different solvers when applied to different sized datasets.

Details

The following solvers can be used to find solutions for a conservation planning problem():

add_default_solver

This solver uses the best software currently installed on the system.

add_gurobi_solver()

Gurobi is a state-of-the-art commercial optimization software with an R package interface. It is by far the fastest of the solvers available for generating prioritizations, however, it is not freely available. That said, licenses are available to academics at no cost. The gurobi package is distributed with the Gurobi software suite. This solver uses the gurobi package to solve problems.

add_cplex_solver()

IBM CPLEX is a commercial optimization software. It is faster than the open source solvers available for generating prioritizations, however, it is not freely available. Similar to the Gurobi software, licenses are available to academics at no cost. This solver uses the cplexAPI package to solve problems using IBM CPLEX.

add_rsymphony_solver()

SYMPHONY is an open-source integer programming solver that is part of the Computational Infrastructure for Operations Research (COIN-OR) project, an initiative to promote development of open-source tools for operations research (a field that includes linear programming). The Rsymphony package provides an interface to COIN-OR and is available on CRAN. This solver uses the Rsymphony package to solve problems.

add_lpsymphony_solver()

The lpsymphony package provides a different interface to the COIN-OR software suite. Unlike the Rsymhpony package, the lpsymphony package is distributed through Bioconductor. The lpsymphony package may be easier to install on Windows or Max OSX systems than the Rsymphony package.

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

Examples

# \dontrun{ # load data data(sim_pu_raster, sim_features) # create basic problem p <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_binary_decisions() # create vector to store plot titles titles <- c() # create empty stack to store solutions s <- stack() # create problem with added rsymphony solver and limit the time spent # searching for the optimal solution to 2 seconds if (require("Rsymphony")) { titles <- c(titles, "Rsymphony (2s)") p1 <- p %>% add_rsymphony_solver(time_limit = 2, verbose = FALSE) s <- addLayer(s, solve(p1)) }
#> Loading required package: Rsymphony
# create problem with added rsymphony solver and limit the time spent # searching for the optimal solution to 5 seconds if (require("Rsymphony")) { titles <- c(titles, "Rsymphony (5s)") p2 <- p %>% add_rsymphony_solver(time_limit = 5, verbose = FALSE) s <- addLayer(s, solve(p2)) } # if the gurobi is installed: create problem with added gurobi solver if (require("gurobi")) { titles <- c(titles, "gurobi (5s)") p3 <- p %>% add_gurobi_solver(time_limit = 5, verbose = FALSE) s <- addLayer(s, solve(p3)) }
#> Loading required package: gurobi
#> Loading required package: slam
# if the cplexAPI is installed: create problem with added cplex solver if (require("cplexAPI")) { titles <- c(titles, "cplexAPI (5s)") p4 <- p %>% add_cplex_solver(time_limit = 5, verbose = FALSE) s <- addLayer(s, solve(p4)) }
#> Loading required package: cplexAPI
# if the lpsymphony is installed: create problem with added lpsymphony solver # note that this solver is skipped on Linux systems due to instability # issues if (require("lpsymphony") & isTRUE(Sys.info()[["sysname"]] != "Linux")) { titles <- c(titles, "lpsymphony") p5 <- p %>% add_lpsymphony_solver(time_limit = 10, verbose = FALSE) s <- addLayer(s, solve(p5)) }
#> Loading required package: lpsymphony
# plot solutions plot(s, main = titles, axes = FALSE, box = FALSE)
# }