Predefined optimization problem
Create a new OptimizationProblem object.
predefined_optimization_problem(x)
x |
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The argument to x must be a list that contains the following
elements:
character model sense.
integer number of features in problem.
integer number of planning units.
integer row indices for problem matrix.
integer column indices for problem matrix.
numeric values for problem matrix.
numeric objective function values.
numeric lower bound for decision values.
numeric upper bound for decision values.
numeric right-hand side values.
numeric constraint senses.
character variable types. These are used to specify
that the decision variables are binary ("B") or continuous
("C").
character identifiers for the rows in the problem
matrix.
character identifiers for the columns in the problem
matrix.
# create list with problem data
l <- list(modelsense = "min", number_of_features = 2,
number_of_planning_units = 3, number_of_zones = 1,
A_i = c(0L, 1L, 0L, 1L, 0L, 1L), A_j = c(0L, 0L, 1L, 1L, 2L, 2L),
A_x = c(2, 10, 1, 10, 1, 10), obj = c(1, 2, 2), lb = c(0, 1, 0),
ub = c(0, 1, 1), rhs = c(2, 10), compressed_formulation = TRUE,
sense = c(">=", ">="), vtype = c("B", "B", "B"),
row_ids = c("spp_target", "spp_target"),
col_ids = c("pu", "pu", "pu"))
# create OptimizationProblem object
x <- predefined_optimization_problem(l)
# print new object
print(x)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.