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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