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.
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:
- class gemseo_pymoo.problems.analytical.knapsack.Knapsack(values, weights, items_ub=None, binary=True, capacity_weight=None, capacity_items=None, initial_guess=None)[source]¶
Bases:
OptimizationProblem
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 theDesignSpace
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.
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.
capacity_items (int | None) – The knapsack number of items capacity. If None, the knapsack will accept an unlimited total number of items.
initial_guess (ndarray | None) – The initial guess for the optimal solution. If None, the initial guess will be an empty knapsack (0, 0, …, 0).
- Raises:
ValueError – Either if the provided arrays do not have the same length or if no capacity is provided.
- AggregationFunction¶
alias of
EvaluationFunction
- class ApproximationMode(value)¶
Bases:
StrEnum
The approximation derivation modes.
- COMPLEX_STEP = 'complex_step'¶
The complex step method used to approximate the Jacobians by perturbing each variable with a small complex number.
- FINITE_DIFFERENCES = 'finite_differences'¶
The finite differences method used to approximate the Jacobians by perturbing each variable with a small real number.
- class DifferentiationMethod(value)¶
Bases:
StrEnum
The differentiation methods.
- COMPLEX_STEP = 'complex_step'¶
- FINITE_DIFFERENCES = 'finite_differences'¶
- NO_DERIVATIVE = 'no_derivative'¶
- USER_GRAD = 'user'¶
- class ProblemType(value)¶
Bases:
StrEnum
The type of problem.
- LINEAR = 'linear'¶
- NON_LINEAR = 'non-linear'¶
- add_callback(callback_func, each_new_iter=True, each_store=False)¶
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 toDatabase.store()
.By default it is set to False.
- Return type:
None
- add_constraint(cstr_func, value=None, cstr_type=None, positive=False)¶
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.cstr_type (MDOFunction.ConstraintType | None) – The type of the constraint.
positive (bool) –
If
True
, then the inequality constraint is positive.By default it is set to False.
- Raises:
TypeError – When the constraint of a linear optimization problem is not an
MDOLinearFunction
.ValueError – When the type of the constraint is missing.
- Return type:
None
- add_eq_constraint(cstr_func, value=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.
- Return type:
None
- add_ineq_constraint(cstr_func, value=None, positive=False)¶
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.positive (bool) –
If
True
, then the inequality constraint is positive.By default it is set to False.
- Return type:
None
- add_observable(obs_func, new_iter=True)¶
Add a function to be observed.
When the
OptimizationProblem
is executed, the observables are called following this sequence:The optimization algorithm calls the objective function with a normalized
x_vect
.The
OptimizationProblem.preprocess_functions()
wraps the function as aNormDBFunction
, which unnormalizes thex_vect
before evaluation.The unnormalized
x_vect
and the result of the evaluation are stored in theOptimizationProblem.database
.The previous step triggers the
OptimizationProblem.new_iter_listeners
, which calls the observables with the unnormalizedx_vect
.The observables themselves are wrapped as a
NormDBFunction
byOptimizationProblem.preprocess_functions()
, but in this case the input is always expected as unnormalized to avoid an additional normalizing-unnormalizing step.Finally, the output is stored in the
OptimizationProblem.database
.
- Parameters:
obs_func (MDOFunction) – An observable to be observed.
new_iter (bool) –
If
True
, then the observable will be called at each new iterate.By default it is set to True.
- Return type:
None
- aggregate_constraint(constraint_index, method=EvaluationFunction.MAX, groups=None, **options)¶
Aggregate a constraint to generate a reduced dimension constraint.
- Parameters:
constraint_index (int) – The index of the constraint in
constraints
.method (Callable[[NDArray[float]], float] | AggregationFunction) –
The aggregation method, e.g.
"max"
,"KS"
or"IKS"
.By default it is set to “MAX”.
groups (Iterable[Sequence[int]] | None) – The groups of components of the constraint to aggregate to produce one aggregation constraint per group of components; if
None
, a single aggregation constraint is produced.**options (Any) – The options of the aggregation method.
- Raises:
ValueError – When the given index is greater or equal than the number of constraints or when the constraint aggregation method is unknown.
- Return type:
None
- apply_exterior_penalty(objective_scale=1.0, scale_inequality=1.0, scale_equality=1.0)¶
Reformulate the optimization problem using exterior penalty.
Given the optimization problem with equality and inequality constraints:
\[ \begin{align}\begin{aligned}min_x f(x)\\s.t.\\g(x)\leq 0\\h(x)=0\\l_b\leq x\leq u_b\end{aligned}\end{align} \]The exterior penalty approach consists in building a penalized objective function that takes into account constraints violations:
\[ \begin{align}\begin{aligned}min_x \tilde{f}(x) = \frac{f(x)}{o_s} + s[\sum{H(g(x))g(x)^2}+\sum{h(x)^2}]\\s.t.\\l_b\leq x\leq u_b\end{aligned}\end{align} \]Where \(H(x)\) is the Heaviside function, \(o_s\) is the
objective_scale
parameter and \(s\) is the scale parameter. The solution of the new problem approximate the one of the original problem. Increasing the values ofobjective_scale
and scale, the solutions are closer but the optimization problem requires more and more iterations to be solved.- Parameters:
scale_equality (float | ndarray) –
The equality constraint scaling constant.
By default it is set to 1.0.
objective_scale (float) –
The objective scaling constant.
By default it is set to 1.0.
scale_inequality (float | ndarray) –
The inequality constraint scaling constant.
By default it is set to 1.0.
- Return type:
None
- change_objective_sign()¶
Change the objective function sign in order to minimize its opposite.
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 an
MDOFunction
.- Return type:
None
- clear_listeners()¶
Clear all the listeners.
- Return type:
None
- evaluate_functions(x_vect=None, eval_jac=False, eval_obj=True, eval_observables=True, normalize=True, no_db_no_norm=False, constraint_names=None, observable_names=None, jacobian_names=None)¶
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.
eval_jac (bool) –
Whether to compute the Jacobian matrices of the functions of interest. If
True
andjacobian_names
isNone
then compute the Jacobian matrices (or gradients) of the functions that are selected for evaluation (witheval_obj
,constraint_names
,eval_observables
and``observable_names``). IfFalse
andjacobian_names
isNone
then no Jacobian matrix is evaluated. Ifjacobian_names
is notNone
then the value ofeval_jac
is ignored.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.
eval_observables (bool) –
Whether to evaluate the observables. If
True
andobservable_names
isNone
then all the observables are evaluated. IfFalse
andobservable_names
isNone
then no observable is evaluated. Ifobservable_names
is notNone
then the value ofeval_observables
is ignored.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.
constraint_names (Iterable[str] | None) – The names of the constraints to evaluate. If
None
then all the constraints are evaluated.observable_names (Iterable[str] | None) – The names of the observables to evaluate. If
None
andeval_observables
isTrue
then all the observables are evaluated. IfNone
andeval_observables
isFalse
then no observable is evaluated.jacobian_names (Iterable[str] | None) – The names of the functions whose Jacobian matrices (or gradients) to compute. If
None
andeval_jac
isTrue
then compute the Jacobian matrices (or gradients) of the functions that are selected for evaluation (witheval_obj
,constraint_names
,eval_observables
and``observable_names``). IfNone
andeval_jac
isFalse
then no Jacobian matrix is computed.
- Returns:
The output values of the functions of interest, as well as their Jacobian matrices if
eval_jac
isTrue
.- Raises:
ValueError – If a name in
jacobian_names
is not the name of a function of the problem.- Return type:
- 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 (ndarray) – The design variables values from the last evaluation.
- Return type:
None
- classmethod from_hdf(file_path, x_tolerance=0.0)¶
Import an optimization history from an HDF file.
- Parameters:
- Returns:
The read optimization problem.
- Return type:
- get_active_ineq_constraints(x_vect, tol=1e-06)¶
For each constraint, indicate if its different components are active.
- Parameters:
- Returns:
For each constraint, a boolean indicator of activation of its different components.
- Return type:
dict[gemseo.core.mdofunctions.mdo_function.MDOFunction, numpy.ndarray]
- get_all_function_name()¶
Retrieve the names of all the function of the optimization problem.
These functions are the constraints, the objective function and the observables.
- 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_best_infeasible_point()¶
Retrieve the best infeasible point within a given tolerance.
- get_constraint_names()¶
Retrieve the names of the constraints.
- get_constraints_number()¶
Retrieve the number of constraints.
- Returns:
The number of constraints.
- Return type:
- get_data_by_names(names, as_dict=True, filter_non_feasible=False)¶
Return the data for specific names of variables.
- Parameters:
- Returns:
The data related to the variables.
- Return type:
- get_design_variable_names()¶
Retrieve the names of the design variables.
- get_dimension()¶
Retrieve the total number of design variables.
- Returns:
The dimension of the design space.
- Return type:
- 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:
- 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:
- get_feasible_points()¶
Retrieve the feasible points within a given tolerance.
This tolerance is defined by
OptimizationProblem.eq_tolerance
for equality constraints andOptimizationProblem.ineq_tolerance
for inequality ones.
- 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:
- get_function_names(names)¶
Return the names of the functions stored in the database.
- 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.- 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:
- 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:
- 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:
- 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:
- get_number_of_unsatisfied_constraints(design_variables, values=mappingproxy({}))¶
Return the number of scalar constraints not satisfied by design variables.
- Parameters:
- Returns:
The number of unsatisfied scalar constraints.
- Return type:
- get_objective_name(standardize=True)¶
Retrieve the name of the objective function.
- get_observable(name)¶
Return an observable of the problem.
- Parameters:
name (str) – The name of the observable.
- Returns:
The pre-processed observable if the functions of the problem have already been pre-processed, otherwise the original one.
- Return type:
- 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[ndarray, ndarray, bool, Dict[str, ndarray], Dict[str, ndarray]]
- get_scalar_constraint_names()¶
Return the names of the scalar constraints.
- 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
- get_x0_normalized(cast_to_real=False)¶
Return the current values of the design variables after normalization.
- has_constraints()¶
Check if the problem has equality or inequality constraints.
- Returns:
True if the problem has equality or inequality constraints.
- Return type:
- has_eq_constraints()¶
Check if the problem has equality constraints.
- Returns:
True if the problem has equality constraints.
- Return type:
- has_ineq_constraints()¶
Check if the problem has inequality constraints.
- Returns:
True if the problem has inequality constraints.
- Return type:
- has_nonlinear_constraints()¶
Check if the problem has non-linear constraints.
- Returns:
True if the problem has equality or inequality constraints.
- Return type:
- 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:
- is_point_feasible(out_val, constraints=None)¶
Check if a point is feasible.
Notes
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.
- Returns:
The feasibility of the point.
- Return type:
- 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, cstr_type, value=None, positive=False)¶
Express a constraint as a string expression.
- Parameters:
func (MDOFunction) – The constraint function.
cstr_type (MDOFunction.ConstraintType) – The type of the constraint.
value (float | None) – The value for which the constraint is active. If
None
, this value is 0.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:
- 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
- to_dataset(name='', categorize=True, opt_naming=True, export_gradients=False, input_values=())¶
Export the database of the optimization problem to a
Dataset
.The variables can be classified into groups:
Dataset.DESIGN_GROUP
orDataset.INPUT_GROUP
for the design variables andDataset.FUNCTION_GROUP
orDataset.OUTPUT_GROUP
for the functions (objective, constraints and observables).- Parameters:
name (str) –
The name to be given to the dataset. If empty, use the name of the
OptimizationProblem.database
.By default it is set to “”.
categorize (bool) –
Whether to distinguish between the different groups of variables. Otherwise, group all the variables in
Dataset.PARAMETER_GROUP`
.By default it is set to True.
opt_naming (bool) –
Whether to use
Dataset.DESIGN_GROUP
andDataset.FUNCTION_GROUP
as groups. Otherwise, useDataset.INPUT_GROUP
andDataset.OUTPUT_GROUP
.By default it is set to True.
export_gradients (bool) –
Whether to export the gradients of the functions (objective function, constraints and observables) if the latter are available in the database of the optimization problem.
By default it is set to False.
input_values (Iterable[ndarray]) –
The input values to be considered. If empty, consider all the input values of the database.
By default it is set to ().
- Returns:
A dataset built from the database of the optimization problem.
- Return type:
- to_hdf(file_path, append=False)¶
Export the optimization problem to an HDF file.
- OPTIM_DESCRIPTION: ClassVar[str] = ['minimize_objective', 'fd_step', 'differentiation_method', 'pb_type', 'ineq_tolerance', 'eq_tolerance']¶
- activate_bound_check: ClassVar[bool] = True¶
Whether to check if a point is in the design space before calling functions.
- property constraint_names: dict[str, list[str]]¶
The standardized constraint names bound to the original ones.
- constraints: list[MDOFunction]¶
The constraints.
- design_space: DesignSpace¶
The design space on which the optimization problem is solved.
- property is_mono_objective: bool¶
Whether the optimization problem is mono-objective.
- Raises:
ValueError – When the dimension of the objective cannot be determined.
- 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: MDOFunction¶
The objective function.
- observables: list[MDOFunction]¶
The observables.
- property parallel_differentiation_options: dict[str, int | bool]¶
The options to approximate the derivatives in parallel.
- pb_type: ProblemType¶
The type of optimization problem.
- solution: OptimizationResult¶
The solution of the optimization problem.
- 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
DriverLibrary
works with this standardized objective and theDatabase
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]¶
Bases:
Knapsack
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 theDesignSpace
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.
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.
capacity_items (int | None) – The knapsack number of items capacity. If None, the knapsack will accept an unlimited total number of items.
initial_guess (ndarray | None) – The initial guess for the optimal solution. If None, the initial guess will be an empty knapsack (0, 0, …, 0).
- AggregationFunction¶
alias of
EvaluationFunction
- class ApproximationMode(value)¶
Bases:
StrEnum
The approximation derivation modes.
- COMPLEX_STEP = 'complex_step'¶
The complex step method used to approximate the Jacobians by perturbing each variable with a small complex number.
- FINITE_DIFFERENCES = 'finite_differences'¶
The finite differences method used to approximate the Jacobians by perturbing each variable with a small real number.
- class DifferentiationMethod(value)¶
Bases:
StrEnum
The differentiation methods.
- COMPLEX_STEP = 'complex_step'¶
- FINITE_DIFFERENCES = 'finite_differences'¶
- NO_DERIVATIVE = 'no_derivative'¶
- USER_GRAD = 'user'¶
- class ProblemType(value)¶
Bases:
StrEnum
The type of problem.
- LINEAR = 'linear'¶
- NON_LINEAR = 'non-linear'¶
- add_callback(callback_func, each_new_iter=True, each_store=False)¶
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 toDatabase.store()
.By default it is set to False.
- Return type:
None
- add_constraint(cstr_func, value=None, cstr_type=None, positive=False)¶
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.cstr_type (MDOFunction.ConstraintType | None) – The type of the constraint.
positive (bool) –
If
True
, then the inequality constraint is positive.By default it is set to False.
- Raises:
TypeError – When the constraint of a linear optimization problem is not an
MDOLinearFunction
.ValueError – When the type of the constraint is missing.
- Return type:
None
- add_eq_constraint(cstr_func, value=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.
- Return type:
None
- add_ineq_constraint(cstr_func, value=None, positive=False)¶
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.positive (bool) –
If
True
, then the inequality constraint is positive.By default it is set to False.
- Return type:
None
- add_observable(obs_func, new_iter=True)¶
Add a function to be observed.
When the
OptimizationProblem
is executed, the observables are called following this sequence:The optimization algorithm calls the objective function with a normalized
x_vect
.The
OptimizationProblem.preprocess_functions()
wraps the function as aNormDBFunction
, which unnormalizes thex_vect
before evaluation.The unnormalized
x_vect
and the result of the evaluation are stored in theOptimizationProblem.database
.The previous step triggers the
OptimizationProblem.new_iter_listeners
, which calls the observables with the unnormalizedx_vect
.The observables themselves are wrapped as a
NormDBFunction
byOptimizationProblem.preprocess_functions()
, but in this case the input is always expected as unnormalized to avoid an additional normalizing-unnormalizing step.Finally, the output is stored in the
OptimizationProblem.database
.
- Parameters:
obs_func (MDOFunction) – An observable to be observed.
new_iter (bool) –
If
True
, then the observable will be called at each new iterate.By default it is set to True.
- Return type:
None
- aggregate_constraint(constraint_index, method=EvaluationFunction.MAX, groups=None, **options)¶
Aggregate a constraint to generate a reduced dimension constraint.
- Parameters:
constraint_index (int) – The index of the constraint in
constraints
.method (Callable[[NDArray[float]], float] | AggregationFunction) –
The aggregation method, e.g.
"max"
,"KS"
or"IKS"
.By default it is set to “MAX”.
groups (Iterable[Sequence[int]] | None) – The groups of components of the constraint to aggregate to produce one aggregation constraint per group of components; if
None
, a single aggregation constraint is produced.**options (Any) – The options of the aggregation method.
- Raises:
ValueError – When the given index is greater or equal than the number of constraints or when the constraint aggregation method is unknown.
- Return type:
None
- apply_exterior_penalty(objective_scale=1.0, scale_inequality=1.0, scale_equality=1.0)¶
Reformulate the optimization problem using exterior penalty.
Given the optimization problem with equality and inequality constraints:
\[ \begin{align}\begin{aligned}min_x f(x)\\s.t.\\g(x)\leq 0\\h(x)=0\\l_b\leq x\leq u_b\end{aligned}\end{align} \]The exterior penalty approach consists in building a penalized objective function that takes into account constraints violations:
\[ \begin{align}\begin{aligned}min_x \tilde{f}(x) = \frac{f(x)}{o_s} + s[\sum{H(g(x))g(x)^2}+\sum{h(x)^2}]\\s.t.\\l_b\leq x\leq u_b\end{aligned}\end{align} \]Where \(H(x)\) is the Heaviside function, \(o_s\) is the
objective_scale
parameter and \(s\) is the scale parameter. The solution of the new problem approximate the one of the original problem. Increasing the values ofobjective_scale
and scale, the solutions are closer but the optimization problem requires more and more iterations to be solved.- Parameters:
scale_equality (float | ndarray) –
The equality constraint scaling constant.
By default it is set to 1.0.
objective_scale (float) –
The objective scaling constant.
By default it is set to 1.0.
scale_inequality (float | ndarray) –
The inequality constraint scaling constant.
By default it is set to 1.0.
- Return type:
None
- change_objective_sign()¶
Change the objective function sign in order to minimize its opposite.
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 an
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.
- compute_knapsack_value(design_variables)¶
Compute the knapsack total value.
- compute_knapsack_weight(design_variables)¶
Compute the knapsack total weight.
- evaluate_functions(x_vect=None, eval_jac=False, eval_obj=True, eval_observables=True, normalize=True, no_db_no_norm=False, constraint_names=None, observable_names=None, jacobian_names=None)¶
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.
eval_jac (bool) –
Whether to compute the Jacobian matrices of the functions of interest. If
True
andjacobian_names
isNone
then compute the Jacobian matrices (or gradients) of the functions that are selected for evaluation (witheval_obj
,constraint_names
,eval_observables
and``observable_names``). IfFalse
andjacobian_names
isNone
then no Jacobian matrix is evaluated. Ifjacobian_names
is notNone
then the value ofeval_jac
is ignored.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.
eval_observables (bool) –
Whether to evaluate the observables. If
True
andobservable_names
isNone
then all the observables are evaluated. IfFalse
andobservable_names
isNone
then no observable is evaluated. Ifobservable_names
is notNone
then the value ofeval_observables
is ignored.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.
constraint_names (Iterable[str] | None) – The names of the constraints to evaluate. If
None
then all the constraints are evaluated.observable_names (Iterable[str] | None) – The names of the observables to evaluate. If
None
andeval_observables
isTrue
then all the observables are evaluated. IfNone
andeval_observables
isFalse
then no observable is evaluated.jacobian_names (Iterable[str] | None) – The names of the functions whose Jacobian matrices (or gradients) to compute. If
None
andeval_jac
isTrue
then compute the Jacobian matrices (or gradients) of the functions that are selected for evaluation (witheval_obj
,constraint_names
,eval_observables
and``observable_names``). IfNone
andeval_jac
isFalse
then no Jacobian matrix is computed.
- Returns:
The output values of the functions of interest, as well as their Jacobian matrices if
eval_jac
isTrue
.- Raises:
ValueError – If a name in
jacobian_names
is not the name of a function of the problem.- Return type:
- 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 (ndarray) – The design variables values from the last evaluation.
- Return type:
None
- classmethod from_hdf(file_path, x_tolerance=0.0)¶
Import an optimization history from an HDF file.
- Parameters:
- Returns:
The read optimization problem.
- Return type:
- get_active_ineq_constraints(x_vect, tol=1e-06)¶
For each constraint, indicate if its different components are active.
- Parameters:
- Returns:
For each constraint, a boolean indicator of activation of its different components.
- Return type:
dict[gemseo.core.mdofunctions.mdo_function.MDOFunction, numpy.ndarray]
- get_all_function_name()¶
Retrieve the names of all the function of the optimization problem.
These functions are the constraints, the objective function and the observables.
- 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_best_infeasible_point()¶
Retrieve the best infeasible point within a given tolerance.
- get_constraint_names()¶
Retrieve the names of the constraints.
- get_constraints_number()¶
Retrieve the number of constraints.
- Returns:
The number of constraints.
- Return type:
- get_data_by_names(names, as_dict=True, filter_non_feasible=False)¶
Return the data for specific names of variables.
- Parameters:
- Returns:
The data related to the variables.
- Return type:
- get_design_variable_names()¶
Retrieve the names of the design variables.
- get_dimension()¶
Retrieve the total number of design variables.
- Returns:
The dimension of the design space.
- Return type:
- 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:
- 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:
- get_feasible_points()¶
Retrieve the feasible points within a given tolerance.
This tolerance is defined by
OptimizationProblem.eq_tolerance
for equality constraints andOptimizationProblem.ineq_tolerance
for inequality ones.
- 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:
- get_function_names(names)¶
Return the names of the functions stored in the database.
- 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.- 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:
- 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:
- 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:
- 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:
- get_number_of_unsatisfied_constraints(design_variables, values=mappingproxy({}))¶
Return the number of scalar constraints not satisfied by design variables.
- Parameters:
- Returns:
The number of unsatisfied scalar constraints.
- Return type:
- get_objective_name(standardize=True)¶
Retrieve the name of the objective function.
- get_observable(name)¶
Return an observable of the problem.
- Parameters:
name (str) – The name of the observable.
- Returns:
The pre-processed observable if the functions of the problem have already been pre-processed, otherwise the original one.
- Return type:
- 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[ndarray, ndarray, bool, Dict[str, ndarray], Dict[str, ndarray]]
- get_scalar_constraint_names()¶
Return the names of the scalar constraints.
- 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
- get_x0_normalized(cast_to_real=False)¶
Return the current values of the design variables after normalization.
- has_constraints()¶
Check if the problem has equality or inequality constraints.
- Returns:
True if the problem has equality or inequality constraints.
- Return type:
- has_eq_constraints()¶
Check if the problem has equality constraints.
- Returns:
True if the problem has equality constraints.
- Return type:
- has_ineq_constraints()¶
Check if the problem has inequality constraints.
- Returns:
True if the problem has inequality constraints.
- Return type:
- has_nonlinear_constraints()¶
Check if the problem has non-linear constraints.
- Returns:
True if the problem has equality or inequality constraints.
- Return type:
- 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:
- is_point_feasible(out_val, constraints=None)¶
Check if a point is feasible.
Notes
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.
- Returns:
The feasibility of the point.
- Return type:
- 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, cstr_type, value=None, positive=False)¶
Express a constraint as a string expression.
- Parameters:
func (MDOFunction) – The constraint function.
cstr_type (MDOFunction.ConstraintType) – The type of the constraint.
value (float | None) – The value for which the constraint is active. If
None
, this value is 0.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:
- 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
- to_dataset(name='', categorize=True, opt_naming=True, export_gradients=False, input_values=())¶
Export the database of the optimization problem to a
Dataset
.The variables can be classified into groups:
Dataset.DESIGN_GROUP
orDataset.INPUT_GROUP
for the design variables andDataset.FUNCTION_GROUP
orDataset.OUTPUT_GROUP
for the functions (objective, constraints and observables).- Parameters:
name (str) –
The name to be given to the dataset. If empty, use the name of the
OptimizationProblem.database
.By default it is set to “”.
categorize (bool) –
Whether to distinguish between the different groups of variables. Otherwise, group all the variables in
Dataset.PARAMETER_GROUP`
.By default it is set to True.
opt_naming (bool) –
Whether to use
Dataset.DESIGN_GROUP
andDataset.FUNCTION_GROUP
as groups. Otherwise, useDataset.INPUT_GROUP
andDataset.OUTPUT_GROUP
.By default it is set to True.
export_gradients (bool) –
Whether to export the gradients of the functions (objective function, constraints and observables) if the latter are available in the database of the optimization problem.
By default it is set to False.
input_values (Iterable[ndarray]) –
The input values to be considered. If empty, consider all the input values of the database.
By default it is set to ().
- Returns:
A dataset built from the database of the optimization problem.
- Return type:
- to_hdf(file_path, append=False)¶
Export the optimization problem to an HDF file.
- OPTIM_DESCRIPTION: ClassVar[str] = ['minimize_objective', 'fd_step', 'differentiation_method', 'pb_type', 'ineq_tolerance', 'eq_tolerance']¶
- activate_bound_check: ClassVar[bool] = True¶
Whether to check if a point is in the design space before calling functions.
- property constraint_names: dict[str, list[str]]¶
The standardized constraint names bound to the original ones.
- constraints: list[MDOFunction]¶
The constraints.
- design_space: DesignSpace¶
The design space on which the optimization problem is solved.
- property is_mono_objective: bool¶
Whether the optimization problem is mono-objective.
- Raises:
ValueError – When the dimension of the objective cannot be determined.
- 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: MDOFunction¶
The objective function.
- observables: list[MDOFunction]¶
The observables.
- property parallel_differentiation_options: dict[str, int | bool]¶
The options to approximate the derivatives in parallel.
- pb_type: ProblemType¶
The type of optimization problem.
- solution: OptimizationResult¶
The solution of the optimization problem.
- 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
DriverLibrary
works with this standardized objective and theDatabase
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 providingobj_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
orMultiObjectiveKnapsack
depending on the
obj_variant
provided.
- An instance of
- 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 ~numpy.random.Generator.integers method of a ~numpy.random.Generator 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 valuessize (int or tuple of ints, optional) – Output shape. If the given shape is, e.g.,
(m, n, k)
, thenm * 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:
out – size-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
See also
random_integers
similar to randint, only for the closed interval [low, high], and 1 is the lowest value if high is omitted.
random.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], [[10], [20]], dtype=np.uint8) array([[ 8, 6, 9, 7], # random [ 1, 16, 9, 12]], dtype=uint8)