OptimizationProblem-methods {oppr}

Optimization problem methods

Description

These functions are used to access data from an OptimizationProblem object.

Usage

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)

Arguments

x

OptimizationProblem object.

Details

The functions return the following data:

nrow

integer number of rows (constraints).

ncol

integer number of columns (decision variables).

ncell

integer number of cells.

modelsense

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

vtype

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

obj

numeric vector defining the linear components of the objective function.

pwlobj

list object defining the piece-wise linear components of the objective function.

A

Matrix::dgCMatrix matrix object defining the problem matrix.

rhs

numeric vector with right-hand-side linear constraints

sense

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

lb

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

ub

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

number_of_projects

integer number of projects in the problem.

number_of_actions

integer number of actions in the problem.

number_of_features

integer number of features in the problem.

number_of_branches

integer number of phylogenetic branches in the problem.

Value

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