| OptimizationProblem-methods | R Documentation |
These functions are used to access data from an OptimizationProblem object.
nrow(x)
## S4 method for signature 'OptimizationProblem'
nrow(x)
ncol(x)
## S4 method for signature 'OptimizationProblem'
ncol(x)
ncell(x)
## S4 method for signature 'OptimizationProblem'
ncell(x)
modelsense(x)
## S4 method for signature 'OptimizationProblem'
modelsense(x)
vtype(x)
## S4 method for signature 'OptimizationProblem'
vtype(x)
obj(x)
## S4 method for signature 'OptimizationProblem'
obj(x)
pwlobj(x)
## S4 method for signature 'OptimizationProblem'
pwlobj(x)
A(x)
## S4 method for signature 'OptimizationProblem'
A(x)
rhs(x)
## S4 method for signature 'OptimizationProblem'
rhs(x)
sense(x)
## S4 method for signature 'OptimizationProblem'
sense(x)
lb(x)
## S4 method for signature 'OptimizationProblem'
lb(x)
ub(x)
## S4 method for signature 'OptimizationProblem'
ub(x)
col_ids(x)
## S4 method for signature 'OptimizationProblem'
col_ids(x)
row_ids(x)
## S4 method for signature 'OptimizationProblem'
row_ids(x)
number_of_branches(x)
## S4 method for signature 'OptimizationProblem'
number_of_branches(x)
get_data(x)
## S4 method for signature 'OptimizationProblem'
get_data(x)
x |
OptimizationProblem object. |
The functions return the following data:
integer number of rows (constraints).
integer number of columns (decision variables).
integer number of cells.
character describing if the problem is to be
maximized ("max") or minimized ("min").
character describing the type of each decision variable:
binary ("B"), semi-continuous ("S"), or continuous
("C")
numeric vector defining the linear components of the
objective function.
list object defining the piece-wise linear components
of the objective function.
Matrix::dgCMatrix matrix object defining the problem matrix.
numeric vector with right-hand-side linear constraints
character vector with the senses of the linear
constraints ("<=", ">=", "=").
numeric lower bound for each decision variable. Missing data
values (NA) indicate no lower bound for a given variable.
numeric upper bounds for each decision variable. Missing
data values (NA) indicate no upper bound for a given variable.
integer number of projects in the problem.
integer number of actions in the problem.
integer number of features in the problem.
integer number of phylogenetic branches in
the problem.
list, Matrix::dgCMatrix, numeric
vector, numeric vector, or scalar integer depending on the
method used.
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