ScalarMOProblem

class desdeo_problem.problem.ScalarMOProblem(objectives, variables, constraints, nadir=None, ideal=None)[source]

Bases: ProblemBase

A multiobjective optimization problem.

To be depreciated.

A multiobjective optimization problem with user defined objective functions, constraints and variables. The objectives each return a single scalar.

Parameters:

objectives (List[ScalarObjective]) – A list containing the objectives of the problem.
variables (List[Variable]) – A list containing the variables of the problem.
constraints (List[ScalarConstraint]) – A list containing the constraints of the problem. If no constraints exist, None may be supllied as the value.
nadir (Optional[np.ndarray]) – The nadir point of the problem.
ideal (Optional[np.ndarray]) – The ideal point of the problem.

__n_of_objectives

The number of objectives in the problem.

Type:: int

__n_of_variables

The number of variables in the problem.

Type:: int

__n_of_constraints

The number of constraints in the problem.

Type:: int

__nadir

The nadir point of the problem.

Type:: np.ndarray

__ideal

The ideal point of the problem.

Type:: np.ndarray

__objectives

A list containing the objectives of the problem.

Type:: List[ScalarObjective]

__constraints

A list conatining the constraints of the problem.

Type:: List[ScalarConstraint]

Raises:: ProblemError – Ill formed nadir and/or ideal vectors are supplied.

Attributes Summary

`constraints`	the list of constraints.
`ideal`	the ideal point of the problem.
`n_of_constraints`	the number of constraints.
`n_of_objectives`	the number of objectives.
`n_of_variables`	the number of variables.
`nadir`	the nadir point of the problem.
`objectives`	the list of objectives.
`variables`	the list of problem variables.

Methods Summary

`evaluate`(decision_vectors[, use_surrogate])	Evaluates the problem using an ensemble of input vectors.
`evaluate_constraint_values`()	Evaluate constraint values.
`get_objective_names`()	Get objective names.
`get_uncertainty_names`()	Return the names of the objectives present in the problem in the order they were added.
`get_variable_bounds`()	Get the variable bounds.
`get_variable_lower_bounds`()	Get variable lower bounds.
`get_variable_names`()	Get variable names.
`get_variable_upper_bounds`()	Get variable upper bounds.

Attributes Documentation

constraints

the list of constraints.

Returns:: the list of constraints
Return type:: List[_ScalarObjective]
Type:: Property

ideal: ndarray

the ideal point of the problem.

Returns:: the ideal point of the problem.
Return type:: np.ndarray
Type:: Property

n_of_constraints

the number of constraints.

Returns:: the number of constraints.
Return type:: int
Type:: Property

n_of_objectives

the number of objectives.

Returns:: the number of objectives.
Return type:: int
Type:: Property

n_of_variables

the number of variables.

Returns:: the number of variables.
Return type:: int
Type:: Property

nadir: ndarray

the nadir point of the problem.

Returns:: the nadir point of the problem.
Return type:: np.ndarray
Type:: Property

objectives

the list of objectives.

Returns:: the list of objectives
Return type:: List[ScalarObjective]
Type:: Property

variables

the list of problem variables.

Returns:: the list of problem variables
Return type:: List[_ScalarObjective]
Type:: Property

Methods Documentation

evaluate(decision_vectors, use_surrogate=False)[source]

Evaluates the problem using an ensemble of input vectors.

Parameters:

decision_vectors (np.ndarray) – An 2D array of decision variable input vectors. Each column represent the values of each decision variable.

Returns:

If constraint are: defined, returns the objective vector values and corresponding constraint values. Or, if no constraints are defined, returns just the objective vector values with None as the constraint values.

Return type:

Tuple[np.ndarray, Union[None, np.ndarray]]

Raises:

ProblemError – The decision_vectors have wrong dimensions.

evaluate_constraint_values()[source]

Evaluate constraint values.

Evaluate just the constraint function values using the attributes decision_vectors and objective_vectors

Raises:: NotImplementedError –
Return type:: Optional[ndarray]

Note

Currently not supported by ScalarMOProblem

get_objective_names()[source]

Get objective names.

Return the names of the objectives present in the problem in the order they were added.

Returns:: Names of the objectives in the order they were added.
Return type:: List[str]

get_uncertainty_names()[source]

Return the names of the objectives present in the problem in the order they were added.

Returns:: Names of the objectives in the order they were added.
Return type:: List[str]

get_variable_bounds()[source]

Get the variable bounds.

Return the upper and lower bounds of each decision variable present in the problem as a 2D numpy array. The first column corresponds to the lower bounds of each variable, and the second column to the upper bound.

Returns:: Lower and upper bounds of each variable as a 2D numpy array. If undefined variables, return None instead.
Return type:: np.ndarray

get_variable_lower_bounds()[source]

Get variable lower bounds.

Return the lower bounds of each variable as a list. The order of the bounds follows the order the variables were added to the problem.

Returns:: An array with the lower bounds of the variables.
Return type:: np.ndarray

get_variable_names()[source]

Get variable names.

Return the variable names of the variables present in the problem in the order they were added.

Returns:: Names of the variables in the order they were added.
Return type:: List[str]

get_variable_upper_bounds()[source]

Get variable upper bounds.

Return the upper bounds of each variable as a list. The order of the bounds follows the order the variables were added to the problem. :returns: An array with the upper bounds of the variables. :rtype: np.ndarray