CADETProcess.optimization.SLSQP

class CADETProcess.optimization.SLSQP(ftol, eps, disp, maxiter, iprint, f_tol, n_max_evals, n_max_iter, finite_diff_rel_step, tol, jac, progress_frequency, x_tol, cv_tol, similarity_tol, parallelization_backend)

Wrapper for the SLSQP 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:

ftolfloat, default 1e-2

Precision goal for the value of f in the stopping criterion.

epsfloat, default 1e-6

Step size used for numerical approximation of the Jacobian.

dispbool, default False

Set to True to print convergence messages. If False, verbosity is ignored and set to 0.

maxiterint, default 1000

Maximum number of iterations.

iprint: int, optional

The verbosity of fmin_slsqp :

iprint <= 0 : Silent operation iprint == 1 : Print summary upon completion (default) iprint >= 2 : Print status of each iterate and summary

Attributes:

aggregated_parameters

dict: Aggregated parameters of the instance.

cv_tol

disp

eps

f_tol

finite_diff_rel_step

ftol

iprint

jac

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)	Returns the optimized bounds of a given optimization_problem as a Bound object.
get_constraint_objects(optimization_problem)	Return constraints as objets.
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.
optimize(optimization_problem[, x0, ...])	Solve OptimizationProblem.
run(optimization_problem[, x0])	Solve the optimization problem using any of the scipy methods.
run_post_processing(X_transformed, ...[, ...])	Run post-processing of generation.

run_final_processing