Optimization (CADETProcess.optimization)

The optimization module provides functionality for minimizing (or maximizing) objective functions, possibly subject to constraints. It includes interfaces to several optimization suites, notably, scipy.optimize and pymoo.

OptimizationProblem

OptimizationProblem(name)

Class for configuring optimization problems.

Optimizer

Base

OptimizerBase(progress_frequency, x_tol, ...)

BaseClass for optimization solver APIs.

Scipy

TrustConstr(gtol, xtol, barrier_tol, ...)	Wrapper for the trust-constr optimization method from the scipy optimization suite.
COBYLA(rhobeg, tol, maxiter, disp, catol, ...)	Wrapper for the COBYLA optimization method from the scipy optimization suite.
NelderMead(maxiter, xatol, fatol, adaptive, ...)	Wrapper for the Nelder-Mead optimization method from the scipy optimization suite.
SLSQP(ftol, eps, disp, maxiter, iprint, ...)	Wrapper for the SLSQP optimization method from the scipy optimization suite.

Pymoo

NSGA2(seed, pop_size, xtol, ftol, cvtol, ...)	Attributes:
U_NSGA3(seed, pop_size, xtol, ftol, cvtol, ...)	Attributes:

Ax

Population

Individual(x, x_transformed, f, f_min, g, ...)	Set of variables evaluated during Optimization.
Population([id])	Collection of Individuals evaluated during Optimization.

Results

OptimizationResults(success, exit_flag, ...)

Optimization results.

Cache

ResultsCache([use_diskcache, directory])

Cache to store (intermediate) results.

ParallelizationBackend

ParallelizationBackendBase(n_cores)	Base class for all parallelization backend adapters.
SequentialBackend(n_cores)	Sequential execution backend for evaluating the target function.
Joblib(verbose, n_cores)	Parallelization backend implementation using joblib.
Pathos(n_cores)	Parallelization backend using the pathos library.