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Optimization problem methods

Source: R/OptimizationProblem-methods.R
OptimizationProblem-methods.Rd

These functions are used to query and update a optimization_problem().

Usage

# S4 method for class 'OptimizationProblem'
nrow(x)

# S4 method for class 'OptimizationProblem'
ncol(x)

# S4 method for class 'OptimizationProblem'
ncell(x)

modelsense(x)

# S4 method for class 'OptimizationProblem'
modelsense(x)

vtype(x)

# S4 method for class 'OptimizationProblem'
vtype(x)

obj(x)

# S4 method for class 'OptimizationProblem'
obj(x)

A(x)

# S4 method for class 'OptimizationProblem'
A(x)

rhs(x)

# S4 method for class 'OptimizationProblem'
rhs(x)

sense(x)

# S4 method for class 'OptimizationProblem'
sense(x)

lb(x)

# S4 method for class 'OptimizationProblem'
lb(x)

ub(x)

# S4 method for class 'OptimizationProblem'
ub(x)

col_ids(x)

# S4 method for class 'OptimizationProblem'
col_ids(x)

row_ids(x)

# S4 method for class 'OptimizationProblem'
row_ids(x)

compressed_formulation(x)

# S4 method for class 'OptimizationProblem'
compressed_formulation(x)

set_obj(x, obj)

# S4 method for class 'OptimizationProblem,ANY'
set_obj(x, obj)

set_lb(x, lb)

# S4 method for class 'OptimizationProblem,ANY'
set_lb(x, lb)

set_ub(x, ub)

# S4 method for class 'OptimizationProblem,ANY'
set_ub(x, ub)

append_linear_constraints(x, rhs, sense, A, row_ids)

# S4 method for class 'OptimizationProblem,ANY,ANY,ANY,ANY'
append_linear_constraints(x, rhs, sense, A, row_ids)

remove_last_linear_constraint(x)

# S4 method for class 'OptimizationProblem'
remove_last_linear_constraint(x)

Arguments

x

optimization_problem() object.

obj

numeric vector containing a new linear coefficient for each decision variable in the problem.

lb

numeric vector containing a new lower bound for each decision variable in the problem.

ub

numeric vector containing a new upper bound for each decision variable in the problem.

rhs

numeric vector with the right-hand-side values for new constraints.

sense

character vector with senses for new constraints (i.e., ">=", "<=", or "=" values).

A

Matrix::sparseMatrix() matrix with coefficients for new constraints.

row_ids

character vector with identifiers for new constraints.

Value

A Matrix::dgCMatrix, numeric vector, numeric vector, or scalar integer depending on the method used.

Details

The following functions are used to query data.

nrow(x)

integer number of rows (constraints).

ncol(x)

integer number of columns (decision variables).

ncell(x)

integer number of cells.

modelsense(x)

character describing if the problem is to be maximized ("max") or minimized ("min").

vtype(x)

character describing the type of each decision variable: binary ("B"), semi-continuous ("S"), or continuous ("C")

obj(x)

numeric vector specifying the objective function.

A(x)

Matrix::dgCMatrix matrix object defining the problem matrix.

rhs(x)

numeric vector with right-hand-side linear constraints

sense(x)

character vector with the senses of the linear constraints ("<=", ">=", "=").

lb(x)

numeric lower bound for each decision variable. Missing data values (NA) indicate no lower bound for a given variable.

ub(x)

numeric upper bounds for each decision variable. Missing data values (NA) indicate no upper bound for a given variable.

number_of_planning_units(x)

integer number of planning units in the problem.

number_of_features(x)

integer number of features the problem.

The following functions are used to update data. Note that these functions return an invisible TRUE indicating success.

set_obj(x, obj)

override the objective in the problem. Here, obj is a numeric vector containing a new linear coefficient for each decision variable in the problem.

set_lb(x, lb)

override the variable lower bounds in the problem. Here, lb is a numeric vector containing a new lower bound.for each decision variable in the problem.

set_ub(x, ub)

override the variable upper bounds in the problem. Here, ub is a numeric vector containing a new upper bound.for each decision variable in the problem.

remove_last_linear_constraint()

remove the last linear constraint added to a problem.

append_linear_constraints(x, A, sense, rhs, row_ids)

add an additional linear constraints to a problem. Here, A is a Matrix::sparseMatrix() matrix, sense is a character vector with constraint senses (i.e., ">=", "<=", or "=" values), rhs is a numeric vector with the right-hand-side values, and row_ids is a character vector with identifiers.