gemseo_pymoo / problems / analytical

# knapsack module¶

Knapsack problem.

This module implements the Knapsack problem.

In its simplest form, it states that:

Given a set of items, each with a given weight and value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given weight capacity and the total value is as large as possible.

\begin{split}\begin{aligned} \text{maximize the total knapsack value } & \sum_{i=1}^{n} value_i * x_i \\ \text{with respect to the design variables }&x_i \\ \text{subject to the general constraints } & \sum_{i=1}^{n} weight_i * x_i \leq capacity_weight\\ & \sum_{i=1}^{n} x_i \leq capacity_items\\ \text{subject to the search domain } & x_i \in \mathbb{N} \end{aligned}\end{split}

Multiple variations of the Knapsack problem can be achieved depending on the inputs provided.

Moreover, a multi-objective version of this problem is also available, in which the following new objective function is added to previous formulation:

$\text{minimize the number of items carried } & \sum_{i=1}^{n} x_i$
class gemseo_pymoo.problems.analytical.knapsack.Knapsack(values, weights, items_ub=None, binary=True, capacity_weight=None, capacity_items=None, initial_guess=None)[source]

Generic knapsack optimization problem.

Different variations can be achieved:

• 0/1 or Binary Knapsack problem:

Given a set of $$n$$ items, each with a weight $$w_i$$ and a value $$v_i$$, and a knapsack with a maximum weight capacity $$W$$. Choose which items to pack in order to maximize the total knapsack value while respecting its weight capacity.

• Unbounded Knapsack problem:

With respect to the Binary variant, it removes the restriction that there is only one of each item. This can be achieved by setting the attribute binary to False, which will remove the upper bound of the design variables.

• Bounded Knapsack problem:

With respect to the Binary variant, it specifies an upper bound for each item. This can be achieved by providing an array items_ub with the upper bound relative to each item.

Moreover, an additional constraint regarding the total number of items can be added. This is achieved through the attribute capacity_items and will limit the number of items that fit into the knapsack.

The constructor.

Initialize the Knapsack OptimizationProblem by defining the DesignSpace and the objective and constraint functions.

The number of items in the problem is deduced from the values array.

Parameters
• values (ndarray) – The items’ values.

• weights (ndarray) – The items’ weights.

• items_ub (ndarray | None) –

The items’ upper bounds. If None, an unlimited number of each item is allowed.

By default it is set to None.

• binary (bool) –

If True, the upper bound of design variables is set to 1.

By default it is set to True.

• capacity_weight (float | None) –

The knapsack weight capacity. If None, the knapsack will have an unlimited weight capacity.

By default it is set to None.

• capacity_items (int | None) –

The knapsack number of items capacity. If None, the knapsack will accept an unlimited total number of items.

By default it is set to None.

• initial_guess (ndarray | None) –

The initial guess for the optimal solution. If None, the initial guess will be an empty knapsack (0, 0, …, 0).

By default it is set to None.

Raises

ValueError – Either if the provided arrays do not have the same length or if no capacity is provided.

Return type

None

Add a callback function after each store operation or new iteration.

Parameters
• callback_func (Callable) – A function to be called after some event.

• each_new_iter (bool) –

If True, then callback at every iteration.

By default it is set to True.

• each_store (bool) –

If True, then callback at every call to Database.store().

By default it is set to False.

Return type

None

Add a constraint (equality and inequality) to the optimization problem.

Parameters
• cstr_func (MDOFunction) – The constraint.

• value (float | None) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

• cstr_type (str | None) –

The type of the constraint. Either equality or inequality.

By default it is set to None.

• positive (bool) –

If True, then the inequality constraint is positive.

By default it is set to False.

Raises
Return type

None

Add an equality constraint to the optimization problem.

Parameters
• cstr_func (MDOFunction) – The constraint.

• value (float | None) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

Return type

None

Add an inequality constraint to the optimization problem.

Parameters
• cstr_func (MDOFunction) – The constraint.

• value (float | None) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

• positive (bool) –

If True, then the inequality constraint is positive.

By default it is set to False.

Return type

None

Add a function to be observed.

When the OptimizationProblem is executed, the observables are called following this sequence:

Parameters
Return type

None

aggregate_constraint(constr_id, method='max', groups=None, **options)

Aggregates a constraint to generate a reduced dimension constraint.

Parameters
• constr_id (int) – The index of the constraint in constraints.

• method (str | Callable[[Callable], Callable]) –

The aggregation method, e.g. "max", "KS" or "IKS".

By default it is set to max.

• groups (tuple[ndarray] | None) –

The groups for which to produce an output. If None, a single output constraint is produced.

By default it is set to None.

• **options (Any) – The options of the aggregation method.

Raises

ValueError – When the given is index is greater or equal than the number of constraints or when the method is aggregation unknown.

change_objective_sign()

The OptimizationProblem expresses any optimization problem as a minimization problem. Then, an objective function originally expressed as a performance function to maximize must be converted into a cost function to minimize, by means of this method.

Return type

None

check()

Check if the optimization problem is ready for run.

Raises

ValueError – If the objective function is missing.

Return type

None

static check_format(input_function)

Check that a function is an instance of MDOFunction.

Parameters

input_function (Any) – The function to be tested.

Raises

TypeError – If the function is not a MDOFunction.

Return type

None

clear_listeners()

Clear all the listeners.

Return type

None

static compute_knapsack_items(design_variables)[source]

Compute the knapsack number of items.

Parameters

design_variables (numpy.ndarray) – The design variables vector.

Returns

The knapsack total number of items.

Return type

numpy.ndarray

compute_knapsack_value(design_variables)[source]

Compute the knapsack total value.

Parameters

design_variables (numpy.ndarray) – The design variables vector.

Returns

The knapsack total value.

Return type

numpy.ndarray

compute_knapsack_weight(design_variables)[source]

Compute the knapsack total weight.

Parameters

design_variables (numpy.ndarray) – The design variables vector.

Returns

The knapsack total weight.

Return type

numpy.ndarray

evaluate_functions(x_vect=None, eval_jac=False, eval_obj=True, eval_observables=False, normalize=True, no_db_no_norm=False)

Compute the functions of interest, and possibly their derivatives.

These functions of interest are the constraints, and possibly the objective.

Some optimization libraries require the number of constraints as an input parameter which is unknown by the formulation or the scenario. Evaluation of initial point allows to get this mandatory information. This is also used for design of experiments to evaluate samples.

Parameters
• x_vect (ndarray) –

The input vector at which the functions must be evaluated; if None, the initial point x_0 is used.

By default it is set to None.

• eval_jac (bool) –

Whether to compute the Jacobian matrices of the functions of interest.

By default it is set to False.

• eval_obj (bool) –

Whether to consider the objective function as a function of interest.

By default it is set to True.

• normalize (bool) –

Whether to consider the input vector x_vect normalized.

By default it is set to True.

• no_db_no_norm (bool) –

If True, then do not use the pre-processed functions, so we have no database, nor normalization.

By default it is set to False.

• eval_observables (bool) –

By default it is set to False.

Returns

The output values of the functions of interest, as well as their Jacobian matrices if eval_jac is True.

Return type

tuple[dict[str, float | ndarray], dict[str, ndarray]]

execute_observables_callback(last_x)

The callback function to be passed to the database.

Call all the observables with the last design variables values as argument.

Parameters

last_x (numpy.ndarray) – The design variables values from the last evaluation.

Return type

None

export_hdf(file_path, append=False)

Export the optimization problem to an HDF file.

Parameters
• file_path (str) – The file to store the data.

• append (bool) –

If True, then the data are appended to the file if not empty.

By default it is set to False.

Return type

None

export_to_dataset(name=None, by_group=True, categorize=True, opt_naming=True, export_gradients=False, input_values=None)

Export the database of the optimization problem to a Dataset.

The variables can be classified into groups: Dataset.DESIGN_GROUP or Dataset.INPUT_GROUP for the design variables and Dataset.FUNCTION_GROUP or Dataset.OUTPUT_GROUP for the functions (objective, constraints and observables).

Parameters
Returns

A dataset built from the database of the optimization problem.

Return type

Dataset

get_active_ineq_constraints(x_vect, tol=1e-06)

For each constraint, indicate if its different components are active.

Parameters
• x_vect (numpy.ndarray) – The vector of design variables.

• tol (float) –

The tolerance for deciding whether a constraint is active.

By default it is set to 1e-06.

Returns

For each constraint, a boolean indicator of activation of its different components.

Return type
get_all_functions()

Retrieve all the functions of the optimization problem.

These functions are the constraints, the objective function and the observables.

Returns

All the functions of the optimization problem.

Return type
get_all_functions_names()

Retrieve the names of all the function of the optimization problem.

These functions are the constraints, the objective function and the observables.

Returns

The names of all the functions of the optimization problem.

Return type

list[str]

get_best_infeasible_point()

Retrieve the best infeasible point within a given tolerance.

Returns

The best infeasible point expressed as the design variables values, the objective function value, the feasibility of the point and the functions values.

Return type

Tuple[Optional[numpy.ndarray], Optional[numpy.ndarray], bool, Dict[str, numpy.ndarray]]

get_constraints_names()

Retrieve the names of the constraints.

Returns

The names of the constraints.

Return type

list[str]

get_constraints_number()

Retrieve the number of constraints.

Returns

The number of constraints.

Return type

int

get_data_by_names(names, as_dict=True, filter_non_feasible=False)

Return the data for specific names of variables.

Parameters
• names (str | Iterable[str]) – The names of the variables.

• as_dict (bool) –

If True, return values as dictionary.

By default it is set to True.

• filter_non_feasible (bool) –

If True, remove the non-feasible points from the data.

By default it is set to False.

Returns

The data related to the variables.

Return type

ndarray | dict[str, ndarray]

get_design_variable_names()

Retrieve the names of the design variables.

Returns

The names of the design variables.

Return type

list[str]

get_dimension()

Retrieve the total number of design variables.

Returns

The dimension of the design space.

Return type

int

get_eq_constraints()

Retrieve all the equality constraints.

Returns

The equality constraints.

Return type
get_eq_constraints_number()

Retrieve the number of equality constraints.

Returns

The number of equality constraints.

Return type

int

get_eq_cstr_total_dim()

Retrieve the total dimension of the equality constraints.

This dimension is the sum of all the outputs dimensions of all the equality constraints.

Returns

The total dimension of the equality constraints.

Return type

int

get_feasible_points()

Retrieve the feasible points within a given tolerance.

This tolerance is defined by OptimizationProblem.eq_tolerance for equality constraints and OptimizationProblem.ineq_tolerance for inequality ones.

Returns

The values of the design variables and objective function for the feasible points.

Return type

tuple[list[ndarray], list[dict[str, float | list[int]]]]

get_function_dimension(name)

Return the dimension of a function of the problem (e.g. a constraint).

Parameters

name (str) – The name of the function.

Returns

The dimension of the function.

Raises
• ValueError – If the function name is unknown to the problem.

• RuntimeError – If the function dimension is not unavailable.

Return type

int

get_function_names(names)

Return the names of the functions stored in the database.

Parameters

names (Iterable[str]) – The names of the outputs or constraints specified by the user.

Returns

The names of the constraints stored in the database.

Return type

list[str]

get_functions_dimensions(names=None)

Return the dimensions of the outputs of the problem functions.

Parameters

names (Iterable[str] | None) –

The names of the functions. If None, then the objective and all the constraints are considered.

By default it is set to None.

Returns

The dimensions of the outputs of the problem functions. The dictionary keys are the functions names and the values are the functions dimensions.

Return type

dict[str, int]

get_ineq_constraints()

Retrieve all the inequality constraints.

Returns

The inequality constraints.

Return type
get_ineq_constraints_number()

Retrieve the number of inequality constraints.

Returns

The number of inequality constraints.

Return type

int

get_ineq_cstr_total_dim()

Retrieve the total dimension of the inequality constraints.

This dimension is the sum of all the outputs dimensions of all the inequality constraints.

Returns

The total dimension of the inequality constraints.

Return type

int

get_nonproc_constraints()

Retrieve the non-processed constraints.

Returns

The non-processed constraints.

Return type
get_nonproc_objective()

Retrieve the non-processed objective function.

Return type

gemseo.core.mdofunctions.mdo_function.MDOFunction

get_number_of_unsatisfied_constraints(design_variables)

Return the number of scalar constraints not satisfied by design variables.

Parameters

design_variables (numpy.ndarray) – The design variables.

Returns

The number of unsatisfied scalar constraints.

Return type

int

get_objective_name(standardize=True)

Retrieve the name of the objective function.

Parameters

standardize (bool) –

Whether to use the name of the objective expressed as a cost, e.g. "-f" when the user seeks to maximize "f".

By default it is set to True.

Returns

The name of the objective function.

Return type

str

get_observable(name)

Retrieve an observable from its name.

Parameters

name (str) – The name of the observable.

Returns

The observable.

Raises

ValueError – If the observable cannot be found.

Return type

gemseo.core.mdofunctions.mdo_function.MDOFunction

get_optimum()

Return the optimum solution within a given feasibility tolerances.

Returns

The optimum result, defined by:

• the value of the objective function,

• the value of the design variables,

• the indicator of feasibility of the optimal solution,

• the value of the constraints,

• the value of the gradients of the constraints.

Return type

Tuple[numpy.ndarray, numpy.ndarray, bool, Dict[str, numpy.ndarray], Dict[str, numpy.ndarray]]

get_scalar_constraints_names()

Return the names of the scalar constraints.

Returns

The names of the scalar constraints.

Return type

list[str]

get_violation_criteria(x_vect)

Compute a violation measure associated to an iteration.

For each constraint, when it is violated, add the absolute distance to zero, in L2 norm.

If 0, all constraints are satisfied

Parameters

x_vect (numpy.ndarray) – The vector of the design variables values.

Returns

The feasibility of the point and the violation measure.

Return type
get_x0_normalized(cast_to_real=False)

Return the current values of the design variables after normalization.

Parameters

cast_to_real (bool) –

Whether to cast the return value to real.

By default it is set to False.

Returns

The current values of the design variables normalized between 0 and 1 from their lower and upper bounds.

Return type

numpy.ndarray

has_constraints()

Check if the problem has equality or inequality constraints.

Returns

True if the problem has equality or inequality constraints.

has_eq_constraints()

Check if the problem has equality constraints.

Returns

True if the problem has equality constraints.

Return type

bool

has_ineq_constraints()

Check if the problem has inequality constraints.

Returns

True if the problem has inequality constraints.

Return type

bool

has_nonlinear_constraints()

Check if the problem has non-linear constraints.

Returns

True if the problem has equality or inequality constraints.

Return type

bool

classmethod import_hdf(file_path, x_tolerance=0.0)

Import an optimization history from an HDF file.

Parameters
• file_path (str) – The file containing the optimization history.

• x_tolerance (float) –

The tolerance on the design variables when reading the file.

By default it is set to 0.0.

Returns

Return type

gemseo.algos.opt_problem.OptimizationProblem

is_max_iter_reached()

Check if the maximum amount of iterations has been reached.

Returns

Whether the maximum amount of iterations has been reached.

Return type

bool

is_point_feasible(out_val, constraints=None)

Check if a point is feasible.

Note

If the value of a constraint is absent from this point, then this constraint will be considered satisfied.

Parameters
• out_val (dict[str, ndarray]) – The values of the objective function, and eventually constraints.

• constraints (Iterable[MDOFunction] | None) –

The constraints whose values are to be tested. If None, then take all constraints of the problem.

By default it is set to None.

Returns

The feasibility of the point.

Return type

bool

preprocess_functions(is_function_input_normalized=True, use_database=True, round_ints=True, eval_obs_jac=False)

Pre-process all the functions and eventually the gradient.

Required to wrap the objective function and constraints with the database and eventually the gradients by complex step or finite differences.

Parameters
• is_function_input_normalized (bool) –

Whether to consider the function input as normalized and unnormalize it before the evaluation takes place.

By default it is set to True.

• use_database (bool) –

Whether to wrap the functions in the database.

By default it is set to True.

• round_ints (bool) –

Whether to round the integer variables.

By default it is set to True.

• eval_obs_jac (bool) –

Whether to evaluate the Jacobian of the observables.

By default it is set to False.

Return type

None

static repr_constraint(func, ctype, value=None, positive=False)

Express a constraint as a string expression.

Parameters
• func (MDOFunction) – The constraint function.

• ctype (str) – The type of the constraint. Either equality or inequality.

• value (float | None) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

• positive (bool) –

If True, then the inequality constraint is positive.

By default it is set to False.

Returns

A string representation of the constraint.

Return type

str

reset(database=True, current_iter=True, design_space=True, function_calls=True, preprocessing=True)

Partially or fully reset the optimization problem.

Parameters
• database (bool) –

Whether to clear the database.

By default it is set to True.

• current_iter (bool) –

Whether to reset the current iteration OptimizationProblem.current_iter.

By default it is set to True.

• design_space (bool) –

Whether to reset the current point of the OptimizationProblem.design_space to its initial value (possibly none).

By default it is set to True.

• function_calls (bool) –

Whether to reset the number of calls of the functions.

By default it is set to True.

• preprocessing (bool) –

Whether to turn the pre-processing of functions to False.

By default it is set to True.

Return type

None

AVAILABLE_PB_TYPES: ClassVar[str] = ['linear', 'non-linear']
COMPLEX_STEP: Final[str] = 'complex_step'
CONSTRAINTS_GROUP: Final[str] = 'constraints'
DESIGN_SPACE_ATTRS: Final[str] = ['u_bounds', 'l_bounds', 'x_0', 'x_names', 'dimension']
DESIGN_SPACE_GROUP: Final[str] = 'design_space'
DESIGN_VAR_NAMES: Final[str] = 'x_names'
DESIGN_VAR_SIZE: Final[str] = 'x_size'
DIFFERENTIATION_METHODS: ClassVar[str] = ['user', 'complex_step', 'finite_differences', 'no_derivatives']
FINITE_DIFFERENCES: Final[str] = 'finite_differences'
FUNCTIONS_ATTRS: ClassVar[str] = ['objective', 'constraints']
GGOBI_FORMAT: Final[str] = 'ggobi'
HDF5_FORMAT: Final[str] = 'hdf5'
LINEAR_PB: Final[str] = 'linear'
NON_LINEAR_PB: Final[str] = 'non-linear'
NO_DERIVATIVES: Final[str] = 'no_derivatives'
OBJECTIVE_GROUP: Final[str] = 'objective'
OBSERVABLES_GROUP: Final[str] = 'observables'
OPTIM_DESCRIPTION: ClassVar[str] = ['minimize_objective', 'fd_step', 'differentiation_method', 'pb_type', 'ineq_tolerance', 'eq_tolerance']
OPT_DESCR_GROUP: Final[str] = 'opt_description'
SOLUTION_GROUP: Final[str] = 'solution'
activate_bound_check: ClassVar[bool] = True

Whether to check if a point is in the design space before calling functions.

capacity_items: int

The knapsack number of items capacity.

capacity_weight: float

The knapsack weight capacity.

constraint_names: dict[str, list[str]]

The standardized constraint names bound to the original ones.

constraints: list[MDOFunction]

The constraints.

database: Database

The database to store the optimization problem data.

design_space: DesignSpace

The design space on which the optimization problem is solved.

property differentiation_method: str

The differentiation method.

property dimension: int

The dimension of the design space.

eq_tolerance: float

The tolerance for the equality constraints.

fd_step: float

The finite differences step.

ineq_tolerance: float

The tolerance for the inequality constraints.

property is_mono_objective: bool

Whether the optimization problem is mono-objective.

minimize_objective: bool

Whether to maximize the objective.

new_iter_observables: list[MDOFunction]

The observables to be called at each new iterate.

nonproc_constraints: list[MDOFunction]

The non-processed constraints.

nonproc_new_iter_observables: list[MDOFunction]

The non-processed observables to be called at each new iterate.

nonproc_objective: MDOFunction

The non-processed objective function.

nonproc_observables: list[MDOFunction]

The non-processed observables.

property objective: gemseo.core.mdofunctions.mdo_function.MDOFunction

The objective function.

observables: list[MDOFunction]

The observables.

property parallel_differentiation: bool

Whether to approximate the derivatives in parallel.

property parallel_differentiation_options: bool

The options to approximate the derivatives in parallel.

pb_type: str

The type of optimization problem.

preprocess_options: dict

The options to pre-process the functions.

solution: OptimizationResult

The solution of the optimization problem.

stop_if_nan: bool

Whether the optimization stops when a function returns NaN.

use_standardized_objective: bool

Whether to use standardized objective for logging and post-processing.

The standardized objective corresponds to the original one expressed as a cost function to minimize. A DriverLib works with this standardized objective and the Database stores its values. However, for convenience, it may be more relevant to log the expression and the values of the original objective.

values: ndarray

The knapsack items’ value.

weights: ndarray

The knapsack items’ weight.

class gemseo_pymoo.problems.analytical.knapsack.MultiObjectiveKnapsack(values, weights, items_ub=None, binary=True, capacity_weight=None, capacity_items=None, initial_guess=None)[source]

Multi-objective Knapsack optimization problem.

With respect to the single-objective Knapsack, it adds an objective relative to the number of items packed. Therefore, besides maximizing the total knapsack value, one must also minimize the total number of items.

All the variations of the Knapsack problem can still be achieved.

The constructor.

Initialize the MultiObjectiveKnapsack OptimizationProblem by defining the DesignSpace and the objective and constraint functions.

The number of items in the problem is deduced from the values array.

Parameters
• values (ndarray) – The items’ values.

• weights (ndarray) – The items’ weights.

• items_ub (ndarray | None) –

The items’ upper bounds. If None, an unlimited number of each item is allowed.

By default it is set to None.

• binary (bool) –

If True, the upper bound of design variables is set to 1.

By default it is set to True.

• capacity_weight (float | None) –

The knapsack weight capacity. If None, the knapsack will have an unlimited weight capacity.

By default it is set to None.

• capacity_items (int | None) –

The knapsack number of items capacity. If None, the knapsack will accept an unlimited total number of items.

By default it is set to None.

• initial_guess (ndarray | None) –

The initial guess for the optimal solution. If None, the initial guess will be an empty knapsack (0, 0, …, 0).

By default it is set to None.

Return type

None

Add a callback function after each store operation or new iteration.

Parameters
• callback_func (Callable) – A function to be called after some event.

• each_new_iter (bool) –

If True, then callback at every iteration.

By default it is set to True.

• each_store (bool) –

If True, then callback at every call to Database.store().

By default it is set to False.

Return type

None

Add a constraint (equality and inequality) to the optimization problem.

Parameters
• cstr_func (MDOFunction) – The constraint.

• value (float | None) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

• cstr_type (str | None) –

The type of the constraint. Either equality or inequality.

By default it is set to None.

• positive (bool) –

If True, then the inequality constraint is positive.

By default it is set to False.

Raises
Return type

None

Add an equality constraint to the optimization problem.

Parameters
• cstr_func (MDOFunction) – The constraint.

• value (float | None) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

Return type

None

Add an inequality constraint to the optimization problem.

Parameters
• cstr_func (MDOFunction) – The constraint.

• value (float | None) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

• positive (bool) –

If True, then the inequality constraint is positive.

By default it is set to False.

Return type

None

Add a function to be observed.

When the OptimizationProblem is executed, the observables are called following this sequence:

Parameters
Return type

None

aggregate_constraint(constr_id, method='max', groups=None, **options)

Aggregates a constraint to generate a reduced dimension constraint.

Parameters
• constr_id (int) – The index of the constraint in constraints.

• method (str | Callable[[Callable], Callable]) –

The aggregation method, e.g. "max", "KS" or "IKS".

By default it is set to max.

• groups (tuple[ndarray] | None) –

The groups for which to produce an output. If None, a single output constraint is produced.

By default it is set to None.

• **options (Any) – The options of the aggregation method.

Raises

ValueError – When the given is index is greater or equal than the number of constraints or when the method is aggregation unknown.

change_objective_sign()

The OptimizationProblem expresses any optimization problem as a minimization problem. Then, an objective function originally expressed as a performance function to maximize must be converted into a cost function to minimize, by means of this method.

Return type

None

check()

Check if the optimization problem is ready for run.

Raises

ValueError – If the objective function is missing.

Return type

None

static check_format(input_function)

Check that a function is an instance of MDOFunction.

Parameters

input_function (Any) – The function to be tested.

Raises

TypeError – If the function is not a MDOFunction.

Return type

None

clear_listeners()

Clear all the listeners.

Return type

None

static compute_knapsack_items(design_variables)

Compute the knapsack number of items.

Parameters

design_variables (numpy.ndarray) – The design variables vector.

Returns

The knapsack total number of items.

Return type

numpy.ndarray

compute_knapsack_value(design_variables)

Compute the knapsack total value.

Parameters

design_variables (numpy.ndarray) – The design variables vector.

Returns

The knapsack total value.

Return type

numpy.ndarray

compute_knapsack_weight(design_variables)

Compute the knapsack total weight.

Parameters

design_variables (numpy.ndarray) – The design variables vector.

Returns

The knapsack total weight.

Return type

numpy.ndarray

evaluate_functions(x_vect=None, eval_jac=False, eval_obj=True, eval_observables=False, normalize=True, no_db_no_norm=False)

Compute the functions of interest, and possibly their derivatives.

These functions of interest are the constraints, and possibly the objective.

Some optimization libraries require the number of constraints as an input parameter which is unknown by the formulation or the scenario. Evaluation of initial point allows to get this mandatory information. This is also used for design of experiments to evaluate samples.

Parameters
• x_vect (ndarray) –

The input vector at which the functions must be evaluated; if None, the initial point x_0 is used.

By default it is set to None.

• eval_jac (bool) –

Whether to compute the Jacobian matrices of the functions of interest.

By default it is set to False.

• eval_obj (bool) –

Whether to consider the objective function as a function of interest.

By default it is set to True.

• normalize (bool) –

Whether to consider the input vector x_vect normalized.

By default it is set to True.

• no_db_no_norm (bool) –

If True, then do not use the pre-processed functions, so we have no database, nor normalization.

By default it is set to False.

• eval_observables (bool) –

By default it is set to False.

Returns

The output values of the functions of interest, as well as their Jacobian matrices if eval_jac is True.

Return type

tuple[dict[str, float | ndarray], dict[str, ndarray]]

execute_observables_callback(last_x)

The callback function to be passed to the database.

Call all the observables with the last design variables values as argument.

Parameters

last_x (numpy.ndarray) – The design variables values from the last evaluation.

Return type

None

export_hdf(file_path, append=False)

Export the optimization problem to an HDF file.

Parameters
• file_path (str) – The file to store the data.

• append (bool) –

If True, then the data are appended to the file if not empty.

By default it is set to False.

Return type

None

export_to_dataset(name=None, by_group=True, categorize=True, opt_naming=True, export_gradients=False, input_values=None)

Export the database of the optimization problem to a Dataset.

The variables can be classified into groups: Dataset.DESIGN_GROUP or Dataset.INPUT_GROUP for the design variables and Dataset.FUNCTION_GROUP or Dataset.OUTPUT_GROUP for the functions (objective, constraints and observables).

Parameters
Returns

A dataset built from the database of the optimization problem.

Return type

Dataset

get_active_ineq_constraints(x_vect, tol=1e-06)

For each constraint, indicate if its different components are active.

Parameters
• x_vect (numpy.ndarray) – The vector of design variables.

• tol (float) –

The tolerance for deciding whether a constraint is active.

By default it is set to 1e-06.

Returns

For each constraint, a boolean indicator of activation of its different components.

Return type
get_all_functions()

Retrieve all the functions of the optimization problem.

These functions are the constraints, the objective function and the observables.

Returns

All the functions of the optimization problem.

Return type
get_all_functions_names()

Retrieve the names of all the function of the optimization problem.

These functions are the constraints, the objective function and the observables.

Returns

The names of all the functions of the optimization problem.

Return type

list[str]

get_best_infeasible_point()

Retrieve the best infeasible point within a given tolerance.

Returns

The best infeasible point expressed as the design variables values, the objective function value, the feasibility of the point and the functions values.

Return type

Tuple[Optional[numpy.ndarray], Optional[numpy.ndarray], bool, Dict[str, numpy.ndarray]]

get_constraints_names()

Retrieve the names of the constraints.

Returns

The names of the constraints.

Return type

list[str]

get_constraints_number()

Retrieve the number of constraints.

Returns

The number of constraints.

Return type

int

get_data_by_names(names, as_dict=True, filter_non_feasible=False)

Return the data for specific names of variables.

Parameters
• names (str | Iterable[str]) – The names of the variables.

• as_dict (bool) –

If True, return values as dictionary.

By default it is set to True.

• filter_non_feasible (bool) –

If True, remove the non-feasible points from the data.

By default it is set to False.

Returns

The data related to the variables.

Return type

ndarray | dict[str, ndarray]

get_design_variable_names()

Retrieve the names of the design variables.

Returns

The names of the design variables.

Return type

list[str]

get_dimension()

Retrieve the total number of design variables.

Returns

The dimension of the design space.

Return type

int

get_eq_constraints()

Retrieve all the equality constraints.

Returns

The equality constraints.

Return type
get_eq_constraints_number()

Retrieve the number of equality constraints.

Returns

The number of equality constraints.

Return type

int

get_eq_cstr_total_dim()

Retrieve the total dimension of the equality constraints.

This dimension is the sum of all the outputs dimensions of all the equality constraints.

Returns

The total dimension of the equality constraints.

Return type

int

get_feasible_points()

Retrieve the feasible points within a given tolerance.

This tolerance is defined by OptimizationProblem.eq_tolerance for equality constraints and OptimizationProblem.ineq_tolerance for inequality ones.

Returns

The values of the design variables and objective function for the feasible points.

Return type

tuple[list[ndarray], list[dict[str, float | list[int]]]]

get_function_dimension(name)

Return the dimension of a function of the problem (e.g. a constraint).

Parameters

name (str) – The name of the function.

Returns

The dimension of the function.

Raises
• ValueError – If the function name is unknown to the problem.

• RuntimeError – If the function dimension is not unavailable.

Return type

int

get_function_names(names)

Return the names of the functions stored in the database.

Parameters

names (Iterable[str]) – The names of the outputs or constraints specified by the user.

Returns

The names of the constraints stored in the database.

Return type

list[str]

get_functions_dimensions(names=None)

Return the dimensions of the outputs of the problem functions.

Parameters

names (Iterable[str] | None) –

The names of the functions. If None, then the objective and all the constraints are considered.

By default it is set to None.

Returns

The dimensions of the outputs of the problem functions. The dictionary keys are the functions names and the values are the functions dimensions.

Return type

dict[str, int]

get_ineq_constraints()

Retrieve all the inequality constraints.

Returns

The inequality constraints.

Return type
get_ineq_constraints_number()

Retrieve the number of inequality constraints.

Returns

The number of inequality constraints.

Return type

int

get_ineq_cstr_total_dim()

Retrieve the total dimension of the inequality constraints.

This dimension is the sum of all the outputs dimensions of all the inequality constraints.

Returns

The total dimension of the inequality constraints.

Return type

int

get_nonproc_constraints()

Retrieve the non-processed constraints.

Returns

The non-processed constraints.

Return type
get_nonproc_objective()

Retrieve the non-processed objective function.

Return type

gemseo.core.mdofunctions.mdo_function.MDOFunction

get_number_of_unsatisfied_constraints(design_variables)

Return the number of scalar constraints not satisfied by design variables.

Parameters

design_variables (numpy.ndarray) – The design variables.

Returns

The number of unsatisfied scalar constraints.

Return type

int

get_objective_name(standardize=True)

Retrieve the name of the objective function.

Parameters

standardize (bool) –

Whether to use the name of the objective expressed as a cost, e.g. "-f" when the user seeks to maximize "f".

By default it is set to True.

Returns

The name of the objective function.

Return type

str

get_observable(name)

Retrieve an observable from its name.

Parameters

name (str) – The name of the observable.

Returns

The observable.

Raises

ValueError – If the observable cannot be found.

Return type

gemseo.core.mdofunctions.mdo_function.MDOFunction

get_optimum()

Return the optimum solution within a given feasibility tolerances.

Returns

The optimum result, defined by:

• the value of the objective function,

• the value of the design variables,

• the indicator of feasibility of the optimal solution,

• the value of the constraints,

• the value of the gradients of the constraints.

Return type

Tuple[numpy.ndarray, numpy.ndarray, bool, Dict[str, numpy.ndarray], Dict[str, numpy.ndarray]]

get_scalar_constraints_names()

Return the names of the scalar constraints.

Returns

The names of the scalar constraints.

Return type

list[str]

get_violation_criteria(x_vect)

Compute a violation measure associated to an iteration.

For each constraint, when it is violated, add the absolute distance to zero, in L2 norm.

If 0, all constraints are satisfied

Parameters

x_vect (numpy.ndarray) – The vector of the design variables values.

Returns

The feasibility of the point and the violation measure.

Return type
get_x0_normalized(cast_to_real=False)

Return the current values of the design variables after normalization.

Parameters

cast_to_real (bool) –

Whether to cast the return value to real.

By default it is set to False.

Returns

The current values of the design variables normalized between 0 and 1 from their lower and upper bounds.

Return type

numpy.ndarray

has_constraints()

Check if the problem has equality or inequality constraints.

Returns

True if the problem has equality or inequality constraints.

has_eq_constraints()

Check if the problem has equality constraints.

Returns

True if the problem has equality constraints.

Return type

bool

has_ineq_constraints()

Check if the problem has inequality constraints.

Returns

True if the problem has inequality constraints.

Return type

bool

has_nonlinear_constraints()

Check if the problem has non-linear constraints.

Returns

True if the problem has equality or inequality constraints.

Return type

bool

classmethod import_hdf(file_path, x_tolerance=0.0)

Import an optimization history from an HDF file.

Parameters
• file_path (str) – The file containing the optimization history.

• x_tolerance (float) –

The tolerance on the design variables when reading the file.

By default it is set to 0.0.

Returns

Return type

gemseo.algos.opt_problem.OptimizationProblem

is_max_iter_reached()

Check if the maximum amount of iterations has been reached.

Returns

Whether the maximum amount of iterations has been reached.

Return type

bool

is_point_feasible(out_val, constraints=None)

Check if a point is feasible.

Note

If the value of a constraint is absent from this point, then this constraint will be considered satisfied.

Parameters
• out_val (dict[str, ndarray]) – The values of the objective function, and eventually constraints.

• constraints (Iterable[MDOFunction] | None) –

The constraints whose values are to be tested. If None, then take all constraints of the problem.

By default it is set to None.

Returns

The feasibility of the point.

Return type

bool

preprocess_functions(is_function_input_normalized=True, use_database=True, round_ints=True, eval_obs_jac=False)

Pre-process all the functions and eventually the gradient.

Required to wrap the objective function and constraints with the database and eventually the gradients by complex step or finite differences.

Parameters
• is_function_input_normalized (bool) –

Whether to consider the function input as normalized and unnormalize it before the evaluation takes place.

By default it is set to True.

• use_database (bool) –

Whether to wrap the functions in the database.

By default it is set to True.

• round_ints (bool) –

Whether to round the integer variables.

By default it is set to True.

• eval_obs_jac (bool) –

Whether to evaluate the Jacobian of the observables.

By default it is set to False.

Return type

None

static repr_constraint(func, ctype, value=None, positive=False)

Express a constraint as a string expression.

Parameters
• func (MDOFunction) – The constraint function.

• ctype (str) – The type of the constraint. Either equality or inequality.

• value (float | None) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

• positive (bool) –

If True, then the inequality constraint is positive.

By default it is set to False.

Returns

A string representation of the constraint.

Return type

str

reset(database=True, current_iter=True, design_space=True, function_calls=True, preprocessing=True)

Partially or fully reset the optimization problem.

Parameters
• database (bool) –

Whether to clear the database.

By default it is set to True.

• current_iter (bool) –

Whether to reset the current iteration OptimizationProblem.current_iter.

By default it is set to True.

• design_space (bool) –

Whether to reset the current point of the OptimizationProblem.design_space to its initial value (possibly none).

By default it is set to True.

• function_calls (bool) –

Whether to reset the number of calls of the functions.

By default it is set to True.

• preprocessing (bool) –

Whether to turn the pre-processing of functions to False.

By default it is set to True.

Return type

None

AVAILABLE_PB_TYPES: ClassVar[str] = ['linear', 'non-linear']
COMPLEX_STEP: Final[str] = 'complex_step'
CONSTRAINTS_GROUP: Final[str] = 'constraints'
DESIGN_SPACE_ATTRS: Final[str] = ['u_bounds', 'l_bounds', 'x_0', 'x_names', 'dimension']
DESIGN_SPACE_GROUP: Final[str] = 'design_space'
DESIGN_VAR_NAMES: Final[str] = 'x_names'
DESIGN_VAR_SIZE: Final[str] = 'x_size'
DIFFERENTIATION_METHODS: ClassVar[str] = ['user', 'complex_step', 'finite_differences', 'no_derivatives']
FINITE_DIFFERENCES: Final[str] = 'finite_differences'
FUNCTIONS_ATTRS: ClassVar[str] = ['objective', 'constraints']
GGOBI_FORMAT: Final[str] = 'ggobi'
HDF5_FORMAT: Final[str] = 'hdf5'
LINEAR_PB: Final[str] = 'linear'
NON_LINEAR_PB: Final[str] = 'non-linear'
NO_DERIVATIVES: Final[str] = 'no_derivatives'
OBJECTIVE_GROUP: Final[str] = 'objective'
OBSERVABLES_GROUP: Final[str] = 'observables'
OPTIM_DESCRIPTION: ClassVar[str] = ['minimize_objective', 'fd_step', 'differentiation_method', 'pb_type', 'ineq_tolerance', 'eq_tolerance']
OPT_DESCR_GROUP: Final[str] = 'opt_description'
SOLUTION_GROUP: Final[str] = 'solution'
activate_bound_check: ClassVar[bool] = True

Whether to check if a point is in the design space before calling functions.

capacity_items: int

The knapsack number of items capacity.

capacity_weight: float

The knapsack weight capacity.

constraint_names: dict[str, list[str]]

The standardized constraint names bound to the original ones.

constraints: list[MDOFunction]

The constraints.

database: Database

The database to store the optimization problem data.

design_space: DesignSpace

The design space on which the optimization problem is solved.

property differentiation_method: str

The differentiation method.

property dimension: int

The dimension of the design space.

eq_tolerance: float

The tolerance for the equality constraints.

fd_step: float

The finite differences step.

ineq_tolerance: float

The tolerance for the inequality constraints.

property is_mono_objective: bool

Whether the optimization problem is mono-objective.

minimize_objective: bool

Whether to maximize the objective.

new_iter_observables: list[MDOFunction]

The observables to be called at each new iterate.

nonproc_constraints: list[MDOFunction]

The non-processed constraints.

nonproc_new_iter_observables: list[MDOFunction]

The non-processed observables to be called at each new iterate.

nonproc_objective: MDOFunction

The non-processed objective function.

nonproc_observables: list[MDOFunction]

The non-processed observables.

property objective: gemseo.core.mdofunctions.mdo_function.MDOFunction

The objective function.

observables: list[MDOFunction]

The observables.

property parallel_differentiation: bool

Whether to approximate the derivatives in parallel.

property parallel_differentiation_options: bool

The options to approximate the derivatives in parallel.

pb_type: str

The type of optimization problem.

preprocess_options: dict

The options to pre-process the functions.

solution: OptimizationResult

The solution of the optimization problem.

stop_if_nan: bool

Whether the optimization stops when a function returns NaN.

use_standardized_objective: bool

Whether to use standardized objective for logging and post-processing.

The standardized objective corresponds to the original one expressed as a cost function to minimize. A DriverLib works with this standardized objective and the Database stores its values. However, for convenience, it may be more relevant to log the expression and the values of the original objective.

values: ndarray

The knapsack items’ value.

weights: ndarray

The knapsack items’ weight.

gemseo_pymoo.problems.analytical.knapsack.create_random_knapsack_problem(n_items, capacity_level=0.1, binary=True, obj_variant='single')[source]

Create a random Knapsack problem.

One can also create a MultiObjectiveKnapsack problem by providing obj_variant = ‘multi’.

The value and the weight of the items are integers randomly generated between 1 and 100.

Parameters
• n_items (int) – The size of the set of items.

• capacity_level (float) –

The percentage of the set of items total weight corresponding to the knapsack capacity.

By default it is set to 0.1.

• binary (bool) –

If True, only one unit of each item is allowed.

By default it is set to True.

• obj_variant (str) –

Single-objective (‘single’) or multi-objective (‘multi’) problem.

By default it is set to single.

Returns

An instance of Knapsack or MultiObjectiveKnapsack depending

on the obj_variant provided.

Raises

ValueError – Either if the number of items is not a positive integer or if the capacity_level is outside the range (0, 1).

Return type
gemseo_pymoo.problems.analytical.knapsack.randint(low, high=None, size=None, dtype=int)

Return random integers from low (inclusive) to high (exclusive).

Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are from [0, low).

Note

New code should use the integers method of a default_rng() instance instead; please see the Quick Start.

Parameters
• low (int or array-like of ints) – Lowest (signed) integers to be drawn from the distribution (unless high=None, in which case this parameter is one above the highest such integer).

• high (int or array-like of ints, optional) – If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None). If array-like, must contain integer values

• size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

• dtype (dtype, optional) –

Desired dtype of the result. Byteorder must be native. The default value is int.

New in version 1.11.0.

Returns

outsize-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided.

Return type

int or ndarray of ints

random_integers

similar to randint, only for the closed interval [low, high], and 1 is the lowest value if high is omitted.

Generator.integers

which should be used for new code.

Examples

>>> np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) # random
>>> np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])


Generate a 2 x 4 array of ints between 0 and 4, inclusive:

>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1], # random
[3, 2, 2, 0]])


Generate a 1 x 3 array with 3 different upper bounds

>>> np.random.randint(1, [3, 5, 10])
array([2, 2, 9]) # random


Generate a 1 by 3 array with 3 different lower bounds

>>> np.random.randint([1, 5, 7], 10)
array([9, 8, 7]) # random


Generate a 2 by 4 array using broadcasting with dtype of uint8

>>> np.random.randint([1, 3, 5, 7], [, ], dtype=np.uint8)
array([[ 8,  6,  9,  7], # random
[ 1, 16,  9, 12]], dtype=uint8)