CADETProcess.optimization.COBYQA

class CADETProcess.optimization.COBYQA(disp, maxfev, maxiter, f_target, feasibility_tol, initial_tr_radius, final_tr_radius, x_tol, cv_nonlincon_tol, n_max_evals, n_max_iter, finite_diff_rel_step, tol, jac, progress_frequency, f_tol, cv_bounds_tol, cv_lineqcon_tol, cv_lincon_tol, similarity_tol, parallelization_backend)

Wrapper for the COBYQA optimization method from the scipy optimization suite.

It defines the solver options in the ‘options’ variable as a dictionary.

Supports:

• Linear constraints

• Linear equality constraints

• Nonlinear constraints

• Bounds

Parameters:

dispbool, default False

Set to True to print information about the optimization procedure.

maxfevint

Maximum number of function evaluations. The default is None.

maxiterint

Maximum number of iterations. The default is None.

f_targetfloat

Target value for the objective function.The optimization procedure is terminated when the objective function value of a feasible point (see feasibility_tol below) is less than or equal to this target. The default is -np.inf

feasibility_tolfloat

Absolute tolerance for the constraint violation. The default is 1e-8

initial_tr_radiusfloat

Initial trust-region radius. Typically, this value should be in the order of one tenth of the greatest expected change to the variables. The default is 1.0

final_tr_radiusfloat

Final trust-region radius. It should indicate the accuracy required in the final values of the variables. If provided, this option overrides the value of tol in the minimize function. The default is 1e-6

Attributes:

aggregated_parameters

dict: Aggregated parameters of the instance.

cv_bounds_tol

cv_lincon_tol

cv_lineqcon_tol

cv_nonlincon_tol

disp

f_target

f_tol

feasibility_tol

final_tr_radius

finite_diff_rel_step

initial_tr_radius

jac

maxfev

maxiter

missing_parameters

list: Parameters that are required but not set.

n_cores

int: Proxy to the number of cores used by the parallelization backend.

n_max_evals

n_max_iter

options

dict: Optimizer options.

parallelization_backend

parameters

dict: Parameters of the instance.

polynomial_parameters

dict: Polynomial parameters of the instance.

progress_frequency

required_parameters

list: Parameters that have no default value.

similarity_tol

sized_parameters

dict: Sized parameters of the instance.

specific_options

dict: Optimizer spcific options.

tol

x_tol

Methods

check_optimization_problem(optimization_problem)	Check if problem is configured correctly and supported by the optimizer.
check_required_parameters()	Verify if all required parameters are set.
check_x0(optimization_problem, x0)	Check the initial guess x0 for an optimization problem.
get_bounds(optimization_problem)	Configure the bound constraints of a given optimization problem.
get_callback(optimization_problem)	Configure callback function.
get_constraint_objects(optimization_problem)	Return the constraints as an object.
get_lincon_obj(optimization_problem)	Return the linear constraints as an object.
get_lineqcon_obj(optimization_problem)	Return the linear equality constraints as an object.
get_nonlincon_obj(optimization_problem)	Return the optimized nonlinear constraints as an object.
load_results(checkpoint_path[, ...])	Load optimization results from a checkpoint file.
optimize(optimization_problem[, x0, ...])	Solve OptimizationProblem.
run_final_processing()	Run post processing at the end of the optimization.
run_post_processing(X_transformed, ...[, ...])	Run post-processing of generation.