OptimizationProblem-methods: Optimization problem methods
In prioritizr: Systematic Conservation Prioritization in R

OptimizationProblem-methods

R Documentation

Optimization problem methods

Description

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

Usage

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

## S4 method for signature 'OptimizationProblem'
ncol(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)

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)

compressed_formulation(x)

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

set_obj(x, obj)

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

set_lb(x, lb)

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

set_ub(x, ub)

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

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

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

remove_last_linear_constraint(x)

## S4 method for signature '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.

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.