gemseo / algos / opt

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lib_nlopt module

NLopt library wrapper.

Warning

If the objective, or a constraint, of the OptimizationProblem returns a value of type int then nlopt.opt.optimize will terminate with ValueError: nlopt invalid argument.

This behavior has been identified as a bug internal to NLopt 2.7.1 and has been fixed in the development version of NLopt.

Until a new version of NLopt including the bugfix is released, the user of GEMSEO shall provide objective and constraint functions that return values of type float and NDArray[float].

class gemseo.algos.opt.lib_nlopt.NLoptAlgorithmDescription(algorithm_name, internal_algorithm_name, library_name='NLopt', description='', website='', handle_integer_variables=False, require_gradient=False, handle_equality_constraints=False, handle_inequality_constraints=False, handle_multiobjective=False, positive_constraints=False, problem_type=ProblemType.NON_LINEAR)[source]

Bases: OptimizationAlgorithmDescription

The description of an optimization algorithm from the NLopt library.

Parameters:
  • algorithm_name (str) –

  • internal_algorithm_name (str) –

  • library_name (str) –

    By default it is set to “NLopt”.

  • description (str) –

    By default it is set to “”.

  • website (str) –

    By default it is set to “”.

  • handle_integer_variables (bool) –

    By default it is set to False.

  • require_gradient (bool) –

    By default it is set to False.

  • handle_equality_constraints (bool) –

    By default it is set to False.

  • handle_inequality_constraints (bool) –

    By default it is set to False.

  • handle_multiobjective (bool) –

    By default it is set to False.

  • positive_constraints (bool) –

    By default it is set to False.

  • problem_type (OptimizationProblem.ProblemType) –

    By default it is set to “non-linear”.

algorithm_name: str

The name of the algorithm in GEMSEO.

internal_algorithm_name: str

The name of the algorithm in the wrapped library.

library_name: str = 'NLopt'

The name of the wrapped library.

class gemseo.algos.opt.lib_nlopt.Nlopt[source]

Bases: OptimizationLibrary

NLopt optimization library interface.

See OptimizationLibrary.

Notes

The missing current values of the DesignSpace attached to the OptimizationProblem are automatically initialized with the method DesignSpace.initialize_missing_current_values().

CTOL_ABS = 'ctol_abs'
FAILURE = 'NLOPT_FAILURE:    Generic failure code'
FORCED_STOP = "NLOPT_FORCED_STOP: Halted because of a forced termination: the user called nlopt_force_stop(opt) on the optimization's nlopt_opt object opt from the user's objective function or constraints."
FTOL_REACHED = 'NLOPT_FTOL_REACHED: Optimization stopped because ftol_rel or ftol_abs (above) was reached'
INIT_STEP = 'init_step'
INNER_MAXEVAL = 'inner_maxeval'
INVALID_ARGS = 'NLOPT_INVALID_ARGS: Invalid arguments (e.g. lower bounds are bigger than upper bounds, an unknown algorithm was specified, etcetera).'
LIBRARY_NAME: ClassVar[str | None] = 'NLopt'

The name of the interfaced library.

LIB_COMPUTE_GRAD = False
MAXEVAL_REACHED = 'NLOPT_MAXEVAL_REACHED: Optimization stopped because maxeval (above) was reached'
MAXTIME_REACHED = 'NLOPT_MAXTIME_REACHED: Optimization stopped because maxtime (above) was reached'
NLOPT_MESSAGES: ClassVar[dict[int, str]] = {-5: "NLOPT_FORCED_STOP: Halted because of a forced termination: the user called nlopt_force_stop(opt) on the optimization's nlopt_opt object opt from the user's objective function or constraints.", -4: 'NLOPT_ROUNDOFF_LIMITED: Halted because roundoff errors limited progress. (In this case, the optimization still typically returns a useful result.)', -3: 'OUT_OF_MEMORY: Ran out of memory', -2: 'NLOPT_INVALID_ARGS: Invalid arguments (e.g. lower bounds are bigger than upper bounds, an unknown algorithm was specified, etcetera).', -1: 'NLOPT_FAILURE:    Generic failure code', 1: 'NLOPT_SUCCESS: Generic success return value', 2: 'NLOPT_STOPVAL_REACHED: Optimization stopped  because stopval (above) was reached', 3: 'NLOPT_FTOL_REACHED: Optimization stopped because ftol_rel or ftol_abs (above) was reached', 4: 'NLOPT_XTOL_REACHED Optimization stopped because xtol_rel or xtol_abs (above) was reached', 5: 'NLOPT_MAXEVAL_REACHED: Optimization stopped because maxeval (above) was reached', 6: 'NLOPT_MAXTIME_REACHED: Optimization stopped because maxtime (above) was reached'}
OUT_OF_MEMORY = 'OUT_OF_MEMORY: Ran out of memory'
ROUNDOFF_LIMITED = 'NLOPT_ROUNDOFF_LIMITED: Halted because roundoff errors limited progress. (In this case, the optimization still typically returns a useful result.)'
STOPVAL = 'stopval'
STOPVAL_REACHED = 'NLOPT_STOPVAL_REACHED: Optimization stopped  because stopval (above) was reached'
SUCCESS = 'NLOPT_SUCCESS: Generic success return value'
XTOL_REACHED = 'NLOPT_XTOL_REACHED Optimization stopped because xtol_rel or xtol_abs (above) was reached'
algo_name: str | None

The name of the algorithm used currently.

descriptions: dict[str, AlgorithmDescription]

The description of the algorithms contained in the library.

internal_algo_name: str | None

The internal name of the algorithm used currently.

It typically corresponds to the name of the algorithm in the wrapped library if any.

opt_grammar: JSONGrammar | None

The grammar defining the options of the current algorithm.

problem: OptimizationProblem

The optimization problem the driver library is bonded to.