Optimization algorithms

In this example, we will discover the different functions of the API related to optimization algorithms.

from gemseo.api import configure_logger
from gemseo.api import get_algorithm_options_schema
from gemseo.api import get_available_opt_algorithms

configure_logger()
<RootLogger root (INFO)>

Get available optimization algorithms

The get_available_opt_algorithms() function returns the list of optimization algorithms available in GEMSEO or in external modules

print(get_available_opt_algorithms())
['NLOPT_MMA', 'NLOPT_COBYLA', 'NLOPT_SLSQP', 'NLOPT_BOBYQA', 'NLOPT_BFGS', 'NLOPT_NEWUOA', 'PDFO_COBYLA', 'PDFO_BOBYQA', 'PDFO_NEWUOA', 'PSEVEN', 'PSEVEN_FD', 'PSEVEN_MOM', 'PSEVEN_NCG', 'PSEVEN_NLS', 'PSEVEN_POWELL', 'PSEVEN_QP', 'PSEVEN_SQP', 'PSEVEN_SQ2P', 'PYMOO_GA', 'PYMOO_NSGA2', 'PYMOO_NSGA3', 'PYMOO_UNSGA3', 'PYMOO_RNSGA3', 'DUAL_ANNEALING', 'SHGO', 'DIFFERENTIAL_EVOLUTION', 'LINEAR_INTERIOR_POINT', 'REVISED_SIMPLEX', 'SIMPLEX', 'SLSQP', 'L-BFGS-B', 'TNC', 'SNOPTB']

Get options schema

For a given optimization algorithm, e.g. "NLOPT_SLSQP", we can get the options; e.g.

print(get_algorithm_options_schema("NLOPT_SLSQP"))
{'$schema': 'http://json-schema.org/draft-04/schema', 'additionalProperties': False, 'type': 'object', 'properties': {'xtol_rel': {'minimum': 0.0, 'description': 'The relative tolerance on the design parameters.', 'type': 'number'}, 'normalize_design_space': {'description': 'If True, normalize the design variables between 0 and 1.', 'type': 'boolean'}, 'xtol_abs': {'minimum': 0.0, 'description': 'The absolute tolerance on the design parameters.', 'type': 'number'}, 'ftol_rel': {'minimum': 0.0, 'description': 'The relative tolerance on the objective function.', 'type': 'number'}, 'ftol_abs': {'minimum': 0.0, 'description': 'The absolute tolerance on the objective function.', 'type': 'number'}, 'max_iter': {'minimum': 1, 'description': 'The maximum number of iterations.', 'type': 'integer'}, 'max_time': {'minimum': 0.0, 'description': 'The maximum runtime in seconds. The value 0 means no runtime limit.', 'type': 'number'}, 'stopval': {'anyOf': [{'description': 'The objective value at which the optimization will stop. Stop minimizing when an objective value :math:`\\leq` stopval is found, or stop maximizing when a value :math:`\\geq` stopval is found. If None, this termination condition will not be active.', 'type': 'null'}, {'description': 'The objective value at which the optimization will stop. Stop minimizing when an objective value :math:`\\leq` stopval is found, or stop maximizing when a value :math:`\\geq` stopval is found. If None, this termination condition will not be active.', 'type': 'number'}]}, 'ctol_abs': {'minimum': 0.0, 'description': 'The absolute tolerance on the constraints.', 'type': 'number'}, 'eq_tolerance': {'minimum': 0.0, 'description': 'The tolerance on the equality constraints.', 'type': 'number'}, 'ineq_tolerance': {'minimum': 0.0, 'description': 'The tolerance on the inequality constraints.', 'type': 'number'}, 'init_step': {'minimum': 0.0, 'description': 'The initial step size for derivative-free algorithms. Increasing init_step will make the initial DOE in COBYLA take wider steps in the design variables. By default, each variable is set to x0 plus a perturbation given by 0.25*(ub_i-x0_i) for i=0, …, len(x0)-1.', 'type': 'number'}, 'inner_maxeval': {'minimum': 0, 'type': 'integer'}, 'kkt_tol_abs': {'anyOf': [{'minimum': 0.0, 'type': 'null'}, {'minimum': 0.0, 'type': 'number'}]}, 'kkt_tol_rel': {'anyOf': [{'minimum': 0.0, 'type': 'null'}, {'minimum': 0.0, 'type': 'number'}]}}, 'required': ['max_iter']}

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