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Optimization problem class

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

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.

See also

Other classes: ConservationModifier-class, ConservationProblem-class, Constraint-class, Decision-class, MultiConservationProblem-class, MultiObjApproach-class, Objective-class, Penalty-class, Portfolio-class, Solver-class, Target-class, TargetMethod-class, Weight-class

Public fields

ptr

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

Methods

Public methods

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$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.