Specify that the
IBM CPLEX software
(IBM 2017) should be used to solve a conservation planning problem()
.
This function can also be used to customize the behavior of the solver.
It requires the cplexAPI package to be installed
(see below for installation instructions).
add_cplex_solver( x, gap = 0.1, time_limit = .Machine$integer.max, presolve = TRUE, threads = 1, verbose = TRUE )
x 


gap 

time_limit 

presolve 

threads 

verbose 

Object (i.e. ConservationProblem
) with the solver
added to it.
IBM CPLEX is a
commercial optimization software. It is faster than
the available open source solvers (e.g. add_lpsymphony_solver()
and
add_rsymphony_solver()
.
Although formal benchmarks examining the performance of this solver for
conservation planning problems have yet to be completed, preliminary
analyses suggest that it performs slightly slower than the Gurobi
solver (i.e. add_gurobi_solver()
).
We recommend using this solver if the Gurobi solver is not available.
Licenses are available for the IBM CPLEX software to academics at no cost
(see https://www.ibm.com/products/ilogcplexoptimizationstudio).
The pkgcplexAPI package is used to interface with IBM CPLEX. To install
this package, the CPLEX_BIN
variable must be set (similar to
the GUROBI_HOME
variable for the Gurobi software) to specify
the file path for the CPLEX software. For example, on a Linux system,
this variable can be specified by adding the following text to the
~/.bashrc
file:
export CPLEX_BIN="/opt/ibm/ILOG/CPLEX_Studio128/cplex/bin/x8664_linux/cplex"
Note that you may need to change the version
number in the file path (i.e. "CPLEX_Studio128"
). For more information
on installing the pkgcplexAPI package, please see the
official installation instructions for the package.
IBM (2017) IBM ILOG CPLEX Optimization Studio CPLEX User's Manual. Version 12 Release 8. IBM ILOG CPLEX Division, Incline Village, NV.
# \dontrun{ # load data data(sim_pu_raster, sim_features) # create problem p < problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_binary_decisions() %>% add_cplex_solver(gap = 0.1, time_limit = 5, verbose = FALSE) # generate solution s < solve(p) # plot solution plot(s, main = "solution", axes = FALSE, box = FALSE)# }