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

OptimizationProblem-class

R Documentation

Optimization problem class

Description

This class is used to represent an optimization problem. It stores the information needed to generate a solution using an exact algorithm solver. Most users should use compile() to generate new optimization problem objects, and the functions distributed with the package to interact with them (e.g., base::as.list()). Only experts should use the fields and methods for this class directly.

Public fields

ptr: A Rcpp::Xptr external pointer. Create a new optimization problem object.

Methods

Public methods

OptimizationProblem$new()
OptimizationProblem$print()
OptimizationProblem$show()
OptimizationProblem$ncol()
OptimizationProblem$nrow()
OptimizationProblem$ncell()
OptimizationProblem$modelsense()
OptimizationProblem$vtype()
OptimizationProblem$obj()
OptimizationProblem$A()
OptimizationProblem$rhs()
OptimizationProblem$sense()
OptimizationProblem$lb()
OptimizationProblem$ub()
OptimizationProblem$number_of_features()
OptimizationProblem$number_of_planning_units()
OptimizationProblem$number_of_zones()
OptimizationProblem$col_ids()
OptimizationProblem$row_ids()
OptimizationProblem$obj_id()
OptimizationProblem$compressed_formulation()
OptimizationProblem$shuffle_columns()
OptimizationProblem$copy()
OptimizationProblem$set_obj()
OptimizationProblem$set_modelsense()
OptimizationProblem$set_lb()
OptimizationProblem$set_ub()
OptimizationProblem$remove_last_linear_constraint()
OptimizationProblem$append_linear_constraints()
OptimizationProblem$clone()

`OptimizationProblem$new()`

Usage

OptimizationProblem$new(ptr)

Arguments

ptr: Rcpp::Xptr external pointer.

Returns

A new OptimizationProblem object.

`OptimizationProblem$print()`

Print concise information about the object.

Usage

OptimizationProblem$print()

Returns

Invisible TRUE.

`OptimizationProblem$show()`

Print concise information about the object.

Usage

OptimizationProblem$show()

Returns

Invisible TRUE.

`OptimizationProblem$ncol()`

Obtain the number of columns in the problem formulation.

Usage

OptimizationProblem$ncol()

Returns

A numeric value.

`OptimizationProblem$nrow()`

Obtain the number of rows in the problem formulation.

Usage

OptimizationProblem$nrow()

Returns

A numeric value.

`OptimizationProblem$ncell()`

Obtain the number of cells in the problem formulation.

Usage

OptimizationProblem$ncell()

Returns

A numeric value.

`OptimizationProblem$modelsense()`

Obtain the model sense.

Usage

OptimizationProblem$modelsense()

Returns

A character value.

`OptimizationProblem$vtype()`

Obtain the decision variable types.

Usage

OptimizationProblem$vtype()

Returns

A character vector.

`OptimizationProblem$obj()`

Obtain the objective function.

Usage

OptimizationProblem$obj()

Returns

A numeric vector.

`OptimizationProblem$A()`

Obtain the constraint matrix.

Usage

OptimizationProblem$A()

Returns

A Matrix::sparseMatrix() object.

`OptimizationProblem$rhs()`

Obtain the right-hand-side constraint values.

Usage

OptimizationProblem$rhs()

Returns

A numeric vector.

`OptimizationProblem$sense()`

Obtain the constraint senses.

Usage

OptimizationProblem$sense()

Returns

A character vector.

`OptimizationProblem$lb()`

Obtain the lower bounds for the decision variables.

Usage

OptimizationProblem$lb()

Returns

A numeric vector.

`OptimizationProblem$ub()`

Obtain the upper bounds for the decision variables.

Usage

OptimizationProblem$ub()

Returns

A numeric vector.

`OptimizationProblem$number_of_features()`

Obtain the number of features.

Usage

OptimizationProblem$number_of_features()

Returns

A numeric value.

`OptimizationProblem$number_of_planning_units()`

Obtain the number of planning units.

Usage

OptimizationProblem$number_of_planning_units()

Returns

A numeric value.

`OptimizationProblem$number_of_zones()`

Obtain the number of zones.

Usage

OptimizationProblem$number_of_zones()

Returns

A numeric value.

`OptimizationProblem$col_ids()`

Obtain the identifiers for the columns.

Usage

OptimizationProblem$col_ids()

Returns

A character value.

`OptimizationProblem$row_ids()`

Obtain the identifiers for the rows.

Usage

OptimizationProblem$row_ids()

Returns

A character value.

`OptimizationProblem$obj_id()`

Obtain the identifiers for the rows.

Usage

OptimizationProblem$obj_id()

Returns

A character value.

`OptimizationProblem$compressed_formulation()`

Is the problem formulation compressed?

Usage

OptimizationProblem$compressed_formulation()

Returns

A logical value.

`OptimizationProblem$shuffle_columns()`

Shuffle the order of the columns in the optimization problem.

Usage

OptimizationProblem$shuffle_columns(order)

Arguments

order: integer vector with new order.

Returns

An integer vector with indices to un-shuffle the problem.

`OptimizationProblem$copy()`

Create a copy of the optimization problem.

Usage

OptimizationProblem$copy()

Returns

A new OptimizationProblem object .

`OptimizationProblem$set_obj()`

Set objective coefficients for the decision variables in the optimization problem.

Usage

OptimizationProblem$set_obj(obj)

Arguments

obj: numeric vector.

Returns

An invisible TRUE.

`OptimizationProblem$set_modelsense()`

Set the model sense for the optimization problem.

Usage

OptimizationProblem$set_modelsense(modelsense)

Arguments

modelsense: character value indicating the model sense. (i.e., either "min" or "max").

Returns

An invisible TRUE.

`OptimizationProblem$set_lb()`

Set lower bounds for the decision variables in the optimization problem.

Usage

OptimizationProblem$set_lb(lb)

Arguments

lb: numeric vector.

Returns

An invisible TRUE.

`OptimizationProblem$set_ub()`

Set upper bounds for the decision variables in the optimization problem.

Usage

OptimizationProblem$set_ub(ub)

Arguments

ub: numeric vector.

Returns

An invisible TRUE.

`OptimizationProblem$remove_last_linear_constraint()`

Remove last linear constraint added to a problem.

Usage

OptimizationProblem$remove_last_linear_constraint()

Returns

An invisible TRUE.

`OptimizationProblem$append_linear_constraints()`

Append linear constraints to the optimization problem.

Usage

OptimizationProblem$append_linear_constraints(rhs, sense, A, row_ids)

Arguments

rhs: numeric vector with right-hand-side values.
sense: character vector with constraint sense values (i.e., "<=", ">=", or "=").
A: Matrix::sparseMatrix() with constraint coefficients.
row_ids: character vector with identifier for constraints.

Returns

An invisible TRUE.

`OptimizationProblem$clone()`

The objects of this class are cloneable with this method.

Usage

OptimizationProblem$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

prioritizr Systematic Conservation Prioritization in R

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