View source: R/optimization_problem.R
| optimization_problem | R Documentation |
Create a new optimization problem.
optimization_problem(x = NULL)
x |
A |
The argument to x can be a NULL or a list. If x is a NULL,
then an empty optimization problem is created. Alternately, if a x is
a list then a fully formulated optimization problem is created.
Specifically, the list should contain 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.
An OptimizationProblem object.
OptimizationProblem-methods.
# create new empty object
x1 <- optimization_problem()
# print new empty object
print(x1)
# create list with optimization problem
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 fully formulated object based on lists
x2 <- optimization_problem(l)
# print fully formulated object
print(x2)
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