add_gurobi_solver: Add a Gurobi solver

Description

Specify that the Gurobi software should be used to solve a project prioritization problem(). This function can also be used to customize the behavior of the solver. In addition to the Gurobi software suite, it also requires the gurobi package to be installed.

Usage

add_gurobi_solver(
  x,
  gap = 0,
  number_solutions = 1,
  solution_pool_method = 2,
  time_limit = .Machine$integer.max,
  presolve = 2,
  threads = 1,
  first_feasible = FALSE,
  verbose = TRUE
)

Value

ProjectProblem object with the solver added to it.

Arguments

x: ProjectProblem object.
gap: numeric gap to optimality. This gap is relative and expresses the acceptable deviance from the optimal objective. 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).
number_solutions: integer number of solutions desired. Defaults to 1. Note that the number of returned solutions can sometimes be less than the argument to number_solutions depending on the argument to solution_pool_method, for example if 100 solutions are requested but only 10 unique solutions exist, then only 10 solutions will be returned.
solution_pool_method: numeric search method identifier that determines how multiple solutions should be generated. Available search modes for generating a portfolio of solutions include: 0 recording all solutions identified whilst trying to find a solution that is within the specified optimality gap, 1 finding one solution within the optimality gap and a number of additional solutions that are of any level of quality (such that the total number of solutions is equal to number_solutions), and 2 finding a specified number of solutions that are nearest to optimality. For more information, see the Gurobi manual (i.e. https://www.gurobi.com/documentation/8.0/refman/poolsearchmode.html#parameter:PoolSearchMode). Defaults to 2.
time_limit: numeric time limit in seconds to run the optimizer. The solver will return the current best solution when this time limit is exceeded.
presolve: integer number indicating how intensively the solver should try to simplify the problem before solving it. The default value of 2 indicates to that the solver should be very aggressive in trying to simplify the problem.
threads: integer number of threads to use for the optimization algorithm. The default value of 1 will result in only one thread being used.
first_feasible: logical should the first feasible solution be be returned? If first_feasible is set to TRUE, the solver will return the first solution it encounters that meets all the constraints, regardless of solution quality. Note that the first feasible solution is not an arbitrary solution, rather it is derived from the relaxed solution, and is therefore often reasonably close to optimality. Defaults to FALSE.
verbose: logical should information be printed while solving optimization problems?

Details

Gurobi is a state-of-the-art commercial optimization software with an R package interface. It is by far the fastest of the solvers supported by this package, however, it is also the only solver that 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.

To install the gurobi package, the Gurobi optimization suite will first need to be installed (see instructions for Linux, Mac OSX, and Windows operating systems). Although Gurobi is a commercial software, academics can obtain a special license for no cost. After installing the Gurobi optimization suite, the gurobi package can then be installed (see instructions for Linux, Mac OSX, and Windows operating systems).

Examples

Run this code

if (FALSE) {
# load data
data(sim_projects, sim_features, sim_actions)

# build problem
p1 <- problem(sim_projects, sim_actions, sim_features,
             "name", "success", "name", "cost", "name") %>%
     add_max_richness_objective(budget = 200) %>%
     add_binary_decisions()

# build another problem, and specify the Gurobi solver
p2 <- p1 %>%
      add_gurobi_solver()

# print problem
print(p2)

# solve problem
s2 <- solve(p2)

# print solution
print(s2)

# plot solution
plot(p2, s2)

# build another problem and obtain multiple solutions
# note that this problem doesn't have 100 unique solutions so
# the solver won't return 100 solutions
p3 <- p1 %>%
      add_gurobi_solver(number_solutions = 100)

# print problem
print(p3)

# solve problem
s3 <- solve(p3)

# print solutions
print(s3)
}