DOE algorithms options

A simple way to solve an OptimizationProblem is to use the API method execute_algo(). E.g.

from gemseo.api import execute_algo
from gemseo.problems.analytical.rosenbrock import Rosenbrock

problem = Rosenbrock()
sol = execute_algo(problem, "OT_LHS", n_samples=20)

They are also used in all MDO and DOE scenarios in the dictionary passed to the MDODiscipline.execute() method.

List of available algorithms : CustomDOE - DiagonalDOE - OT_AXIAL - OT_COMPOSITE - OT_FACTORIAL - OT_FAURE - OT_FULLFACT - OT_HALTON - OT_HASELGROVE - OT_LHS - OT_LHSC - OT_MONTE_CARLO - OT_RANDOM - OT_REVERSE_HALTON - OT_SOBOL - OT_SOBOL_INDICES - bbdesign - ccdesign - ff2n - fullfact - lhs - pbdesign -

CustomDOE

Description

The CustomDOE class is used for creation of DOE samples provided by user. This samples are provided as file in text or csv format.

Options

  • comments, str - the characters or list of characters used to indicate the start of a comment

  • delimiter, str - The string used to separate values.

  • doe_file, str - path and name of file

  • eval_jac, bool - evaluate jacobian

  • n_processes, int - number of processes

  • skiprows, int - skip the first skiprows lines

  • wait_time_between_samples, float - waiting time between two samples

DiagonalDOE

Description

Diagonal design of experiments

Options

  • eval_jac, bool - evaluate jacobian

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • reverse, list(str) - list of dimensions or variables to sample from their upper bounds to their lower bounds. Default: None.

  • wait_time_between_samples, float - waiting time between two samples

OT_AXIAL

Description

Axial design implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_COMPOSITE

Description

Composite design implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_FACTORIAL

Description

Factorial design implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_FAURE

Description

Faure sequence implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_FULLFACT

Description

Full factorial design implementedin openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_HALTON

Description

Halton sequence implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_HASELGROVE

Description

Haselgrove sequence implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_LHS

Description

Latin Hypercube Sampling implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_LHSC

Description

Centered Latin Hypercube Sampling implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_MONTE_CARLO

Description

Monte Carlo sequence implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_RANDOM

Description

Random sampling implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_REVERSE_HALTON

Description

Reverse Halton sequence implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_SOBOL

Description

Sobol sequence implemented in openTURNS library

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

OT_SOBOL_INDICES

Description

Sobol indices

Options

  • centers, array - centers for axial, factorial and composite designs

  • distribution_name, str - distribution name

  • end, float - level end for trapezoidal distribution

  • eval_jac, bool - evaluate jacobian

  • levels, array - levels for axial, factorial and composite designs

  • mu, float - mean of a random variable for beta, normal and truncated normal distributions

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • sigma, float - standard deviation for beta, normal and truncated normal distributions

  • start, float - level start for trapezoidal distribution

  • wait_time_between_samples, float - waiting time between two samples

bbdesign

Description

Box-Behnken design implemented in pyDOE

Options

  • alpha, str - effect the variance, either “orthogonal” or “rotatable”

  • center_bb, int - number of center points for Box-Behnken design

  • center_cc, tuple - 2-tuple of center points for the central composite design

  • criterion, :code:`` - Default value = None)

  • eval_jac, bool - evaluate jacobian

  • face, str - The relation between the start points and the corner (factorial) points, either “circumscribed”, “inscribed” or “faced”

  • iterations, :code:`` - Default value = 5)

  • levels, array - levels for axial, factorial and composite designs

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • wait_time_between_samples, float - waiting time between two samples

ccdesign

Description

Central Composite implemented in pyDOE

Options

  • alpha, str - effect the variance, either “orthogonal” or “rotatable”

  • center_bb, int - number of center points for Box-Behnken design

  • center_cc, tuple - 2-tuple of center points for the central composite design

  • criterion, :code:`` - Default value = None)

  • eval_jac, bool - evaluate jacobian

  • face, str - The relation between the start points and the corner (factorial) points, either “circumscribed”, “inscribed” or “faced”

  • iterations, :code:`` - Default value = 5)

  • levels, array - levels for axial, factorial and composite designs

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • wait_time_between_samples, float - waiting time between two samples

ff2n

Description

2-Level Full-Factorial implemented in pyDOE

Options

  • alpha, str - effect the variance, either “orthogonal” or “rotatable”

  • center_bb, int - number of center points for Box-Behnken design

  • center_cc, tuple - 2-tuple of center points for the central composite design

  • criterion, :code:`` - Default value = None)

  • eval_jac, bool - evaluate jacobian

  • face, str - The relation between the start points and the corner (factorial) points, either “circumscribed”, “inscribed” or “faced”

  • iterations, :code:`` - Default value = 5)

  • levels, array - levels for axial, factorial and composite designs

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • wait_time_between_samples, float - waiting time between two samples

fullfact

Description

Full-Factorial implemented in pyDOE

Options

  • alpha, str - effect the variance, either “orthogonal” or “rotatable”

  • center_bb, int - number of center points for Box-Behnken design

  • center_cc, tuple - 2-tuple of center points for the central composite design

  • criterion, :code:`` - Default value = None)

  • eval_jac, bool - evaluate jacobian

  • face, str - The relation between the start points and the corner (factorial) points, either “circumscribed”, “inscribed” or “faced”

  • iterations, :code:`` - Default value = 5)

  • levels, array - levels for axial, factorial and composite designs

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • wait_time_between_samples, float - waiting time between two samples

lhs

Description

Latin Hypercube Sampling implemented in pyDOE

Options

  • alpha, str - effect the variance, either “orthogonal” or “rotatable”

  • center_bb, int - number of center points for Box-Behnken design

  • center_cc, tuple - 2-tuple of center points for the central composite design

  • criterion, :code:`` - Default value = None)

  • eval_jac, bool - evaluate jacobian

  • face, str - The relation between the start points and the corner (factorial) points, either “circumscribed”, “inscribed” or “faced”

  • iterations, :code:`` - Default value = 5)

  • levels, array - levels for axial, factorial and composite designs

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • wait_time_between_samples, float - waiting time between two samples

pbdesign

Description

Plackett-Burman design implemented in pyDOE

Options

  • alpha, str - effect the variance, either “orthogonal” or “rotatable”

  • center_bb, int - number of center points for Box-Behnken design

  • center_cc, tuple - 2-tuple of center points for the central composite design

  • criterion, :code:`` - Default value = None)

  • eval_jac, bool - evaluate jacobian

  • face, str - The relation between the start points and the corner (factorial) points, either “circumscribed”, “inscribed” or “faced”

  • iterations, :code:`` - Default value = 5)

  • levels, array - levels for axial, factorial and composite designs

  • n_processes, int - number of processes

  • n_samples, int - number of samples

  • wait_time_between_samples, float - waiting time between two samples