CADETProcess.optimization.OptimizationProblem

class CADETProcess.optimization.OptimizationProblem(name)

Class for configuring optimization problems.

Stores information about - optimization variables - objectives - linear and nonlinear constraints - callbacks - meta scores - multi-criteria-decision functions

Attributes:

namestr

Name of the optimization problem

evaluation_objectslist

list: Objects to be evaluated during optimization.

evaluatorsobj

list: Evaluators in OptimizationProblem.

cacheResultsCache

Cache to store (intermediate) results.

variableslist

list: List of all optimization variables.

objectives: list

Objective functions.

nonlinear_constraints: list of callables

Nonlinear constraint functions.

linear_constraintslist

list: Linear inequality constraints of OptimizationProblem.

linear_equality_constraintslist

list: Linear equality constraints of OptimizationProblem.

callbackslist

list: Callback functions for recording progress.

meta_scores: list

Meta score functions.

multi_criteria_decision_functionslist

list: Multi criteria decision functions.

Methods

add_callback(callback[, name, ...])	Add callback function for processing (intermediate) results.
add_evaluation_object(evaluation_object[, name])	Add evaluation object to the optimization problem.
add_evaluator(evaluator[, name, args, kwargs])	Add Evaluator to OptimizationProblem.
add_linear_constraint(opt_vars[, lhs, b])	Add linear inequality constraints.
add_linear_equality_constraint(opt_vars[, ...])	Add linear equality constraints.
add_meta_score(meta_score[, name, ...])	Add Meta score to the OptimizationProblem.
add_multi_criteria_decision_function(...[, name])	Add multi criteria decision function to OptimizationProblem.
add_nonlinear_constraint(nonlincon[, name, ...])	Add nonliner constraint function to optimization problem.
add_objective(objective[, name, ...])	Add objective function to optimization problem.
add_variable(name[, evaluation_objects, ...])	Add optimization variable to the OptimizationProblem.
add_variable_dependency(dependent_variable, ...)	Add dependency between two optimization variables.
check_bounds(x)	Check if all bound constraints are kept.
check_config([ignore_linear_constraints])	Check if the OptimizationProblem is configured correctly.
check_duplicate_variables()	Raise warning if duplicate variables exist.
check_individual(x[, silent])	Check if individual is valid.
check_linear_constraints(x)	Check if linear inequality constraints are met at point x.
check_linear_constraints_dependency()	Check that variables used in linear constraints are independent.
check_linear_constraints_transforms()	Check that variables used in linear constraints only use linear transforms.
check_linear_equality_constraints(x)	Check if linear equality constraints are met at point x.
check_nonlinear_constraints(x)	Check if all nonlinear constraints are met.
check_required_parameters()	Verify if all required parameters are set.
create_hopsy_problem([...])	Creates a hopsy problem from the optimization problem.
create_individual(x[, f, g, m, f_min, cv, ...])	Create new individual from data.
create_initial_values([n_samples, seed, ...])	Create initial value within parameter space.
create_population(X[, F, G, M, F_min, CV, ...])	Create new population from data.
delete_cache([reinit])	Delete cache with (intermediate) results.
ensures2d()	Make sure population is ndarray with ndmin=2.
ensures_minimization()	Convert maximization problems to minimization problems.
evaluate_callbacks(ind[, current_iteration, ...])	Evaluate callback functions at point x.
evaluate_callbacks_population(population[, ...])	Evaluate callbacks for each individual ind in population.
evaluate_linear_constraints(x)	Calculate value of linear inequality constraints at point x.
evaluate_linear_equality_constraints(x)	Calculate value of linear equality constraints at point x.
evaluate_meta_scores(x[, force])	Evaluate meta functions at point x.
evaluate_meta_scores_population(population)	Evaluate meta score functions for each point x in population.
evaluate_multi_criteria_decision_functions(...)	Evaluate evaluate multi criteria decision functions.
evaluate_nonlinear_constraints(x[, force])	Evaluate nonlinear constraint functions at point x.
evaluate_nonlinear_constraints_population(...)	Evaluate nonlinear constraint for each point x in population.
evaluate_nonlinear_constraints_violation(x)	Evaluate nonlinear constraints violation at point x.
evaluate_nonlinear_constraints_violation_population(...)	Evaluate nonlinear constraints violation for each point x in population.
evaluate_objectives(x[, force])	Evaluate objective functions at point x.
evaluate_objectives_population(population[, ...])	Evaluate objective functions for each point x in population.
get_chebyshev_center([...])	Compute chebychev center.
get_dependent_values(x)	Determine values of dependent optimization variables.
get_independent_values(x)	Remove dependent values from x.
gets_dependent_values()	Get dependent values of individual before calling function.
nonlinear_constraint_jacobian(x[, dx])	Compute jacobian of the nonlinear constraints at point x.
objective_jacobian(x[, ensure_minimization, dx])	Compute jacobian of objective functions using finite differences.
prune_cache([tag])	Prune cache with (intermediate) results.
remove_linear_constraint(index)	Remove linear inequality constraint.
remove_linear_equality_constraint(index)	Remove linear equality constraint.
remove_variable(var_name)	Remove optimization variable from the OptimizationProblem.
set_variables(x[, evaluation_objects])	Set the values from the x-vector to the EvaluationObjects.
setup_cache()	Setup cache to store (intermediate) results.
transform(x_independent)	Transform the independent optimization variables from untransformed parameter space.
transform_maximization(s, scores)	Transform maximization problems to minimization problems.
untransform(x_transformed)	Untransform the optimization variables from transformed parameter space.
untransforms()	Untransform population or individual before calling function.