add_lpsolveapi_solver: Add a lp_solve solver with lpSolveAPI

Description

Specify that the lp_solve software should be used to solve a project prioritization problem using the lpSolveAPI package. This function can also be used to customize the behavior of the solver. It requires the lpSolveAPI package.

Usage

add_lpsolveapi_solver(x, gap = 0, presolve = FALSE, verbose = TRUE)

Arguments

ProjectProblem-class object.

gap

numeric gap to optimality. This gap is relative when solving problems using gurobi, and will cause the optimizer to terminate when the difference between the upper and lower objective function bounds is less than the gap times the upper bound. For example, a value of 0.01 will result in the optimizer stopping when the difference between the bounds is 1 percent of the upper bound. For other solvers, this is the absolute gap, so if the optimal value for a maximization problem is 10, a gap of 0.01 means that a solution between 10 and 10.01 is required. Defaults to 0, so that optimal solutions will be returned.

presolve

logical indicating if attempts to should be made to simplify the optimization problem (TRUE) or not (FALSE). Defaults to TRUE.

verbose

logical should information be printed while solving optimization problems?

Value

ProjectProblem-class object with the solver added to it.

Details

lp_solve is an open-source integer programming solver. Although this solver is the slowest currently supported solver, it is also the only exact algorithm solver that can be installed on all operating systems without any manual installation steps. This solver is provided so that users can try solving small project prioritization problems, without needing to install additional software. When solve moderate or large project prioritization problems, consider using add_gurobi_solver.

Examples

Run this code

# NOT RUN {
# load data
data(sim_projects, sim_features, sim_actions)

# build problem with lpSolveAPI solver
p <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_max_richness_objective(budget = 200) %>%
     add_binary_decisions() %>%
     add_lpsolveapi_solver()

# print problem
print(p)

# solve problem
s <- solve(p)

# print solution
print(s)

# plot solution
plot(p, s)
# }