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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


Method new()

Usage

Arguments

ptr

Rcpp::Xptr external pointer.

Returns

A new OptimizationProblem object.


Method print()

Print concise information about the object.

Usage

OptimizationProblem$print()

Returns

Invisible TRUE.


Method show()

Print concise information about the object.

Usage

OptimizationProblem$show()

Returns

Invisible TRUE.


Method ncol()

Obtain the number of columns in the problem formulation.

Usage

OptimizationProblem$ncol()

Returns

A numeric value.


Method nrow()

Obtain the number of rows in the problem formulation.

Usage

OptimizationProblem$nrow()

Returns

A numeric value.


Method ncell()

Obtain the number of cells in the problem formulation.

Usage

OptimizationProblem$ncell()

Returns

A numeric value.


Method modelsense()

Obtain the model sense.

Usage

OptimizationProblem$modelsense()

Returns

A character value.


Method vtype()

Obtain the decision variable types.

Usage

OptimizationProblem$vtype()

Returns

A character vector.


Method obj()

Obtain the objective function.

Usage

OptimizationProblem$obj()

Returns

A numeric vector.


Method A()

Obtain the constraint matrix.

Usage

OptimizationProblem$A()

Returns

A Matrix::sparseMatrix() object.


Method rhs()

Obtain the right-hand-side constraint values.

Usage

OptimizationProblem$rhs()

Returns

A numeric vector.


Method sense()

Obtain the constraint senses.

Usage

OptimizationProblem$sense()

Returns

A character vector.


Method lb()

Obtain the lower bounds for the decision variables.

Usage

OptimizationProblem$lb()

Returns

A numeric vector.


Method ub()

Obtain the upper bounds for the decision variables.

Usage

OptimizationProblem$ub()

Returns

A numeric vector.


Method number_of_features()

Obtain the number of features.

Usage

OptimizationProblem$number_of_features()

Returns

A numeric value.


Method number_of_planning_units()

Obtain the number of planning units.

Usage

OptimizationProblem$number_of_planning_units()

Returns

A numeric value.


Method number_of_zones()

Obtain the number of zones.

Usage

OptimizationProblem$number_of_zones()

Returns

A numeric value.


Method col_ids()

Obtain the identifiers for the columns.

Usage

OptimizationProblem$col_ids()

Returns

A character value.


Method row_ids()

Obtain the identifiers for the rows.

Usage

OptimizationProblem$row_ids()

Returns

A character value.


Method compressed_formulation()

Is the problem formulation compressed?

Usage

OptimizationProblem$compressed_formulation()

Returns

A logical value.


Method 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.


Method copy()

Create a copy of the optimization problem.

Usage

OptimizationProblem$copy()

Returns

A new OptimizationProblem object .


Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizationProblem$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.