CADETProcess.optimization.NelderMead

class CADETProcess.optimization.NelderMead(maxiter, xatol, fatol, adaptive, disp, x_tol, f_tol, n_max_evals, n_max_iter, finite_diff_rel_step, tol, jac, progress_frequency, cv_tol, similarity_tol, parallelization_backend)

Wrapper for the Nelder-Mead optimization method from the scipy optimization suite.

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

Parameters:

maxiterUnsignedInteger

Maximum allowed number of iterations. The default = 1000.

initial_simplexNone or array_like, optional

Initial simplex. If given, it overrides x0. initial_simplex[j, :] should contain the coordinates of the jth vertex of the N+1 vertices in the simplex, where N is the dimension.

xatolUnsignedFloat, optional

Absolute error in xopt between iterations that is acceptable for convergence.

fatolUnsignedFloat, optional

Absolute error in f(xopt) between iterations that is acceptable for convergence.

adaptiveBool, optional

Adapt algorithm parameters to dimensionality of the problem. Useful for high-dimensional minimization.

dispBool, optional

Set to True to print convergence messages.

Attributes:

adaptive

aggregated_parameters

dict: Aggregated parameters of the instance.

cv_tol

disp

f_tol

fatol

finite_diff_rel_step

initial_simplex

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

xatol

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