Use a solver for a list of constraints.

Use a mathematical programming solver to solve a list for constrains.

Usage

useSolver(
  allConstraints,
  solver = c("GLPK", "lpSolve", "Gurobi", "Symphony"),
  timeLimit = Inf,
  formNames = NULL,
  ...
)

Arguments

allConstraints: List of constraints.
solver: A character string indicating the solver to use.
timeLimit: The maximal runtime in seconds.
formNames: A character vector with names to give to the forms.
...: Additional arguments for the solver.

Value

A list with the following elements:

solution_found: Was a solution found?
solution: Numeric vector containing the found solution.
solution_status: Was the solution optimal?

Details

Wrapper around the functions of different solvers (gurobi::gurobi(), lpSolve::lp(), ... for a list of constraints set up via eatATA. Rglpk is used per default.

Additional arguments can be passed through ... and vary from solver to solver (see their respective help pages, lp or Rglpk_solve_LP); for example time limits can not be set for lpSolve.

Examples

nForms <- 2
nItems <- 4

# create constraits
target <- minimaxObjective(nForms = nForms, c(1, 0.5, 1.5, 2),
                       targetValue = 2, itemIDs = 1:nItems)
noItemOverlap <- itemUsageConstraint(nForms, operator = "=", itemIDs = 1:nItems)
testLength <- itemsPerFormConstraint(nForms = nForms,
                           operator = "<=", targetValue = 2, itemIDs = 1:nItems)

# use a solver
result <- useSolver(list(target, noItemOverlap, testLength),
  itemIDs = paste0("Item_", 1:4),
  solver = "GLPK")
#> GLPK Simplex Optimizer 5.0
#> 10 rows, 9 columns, 36 non-zeros
#>       0: obj =   0.000000000e+00 inf =   8.000e+00 (6)
#>       6: obj =   5.000000000e-01 inf =   0.000e+00 (0)
#> *     9: obj =   5.000000000e-01 inf =   0.000e+00 (0)
#> OPTIMAL LP SOLUTION FOUND
#> GLPK Integer Optimizer 5.0
#> 10 rows, 9 columns, 36 non-zeros
#> 8 integer variables, all of which are binary
#> Integer optimization begins...
#> Long-step dual simplex will be used
#> +     9: mip =     not found yet >=              -inf        (1; 0)
#> +     9: >>>>>   5.000000000e-01 >=   5.000000000e-01   0.0% (1; 0)
#> +     9: mip =   5.000000000e-01 >=     tree is empty   0.0% (0; 1)
#> INTEGER OPTIMAL SOLUTION FOUND
#> Optimal solution found.