OptimizationAnalysis¶

class persalys.OptimizationAnalysis(*args)¶

Perform the optimization analysis of a model.

Parameters:

namestr: Name
physicalModelPhysicalModel: Physical model
solverNamestr: Optimization algorithm name (optional). Use GetSolverNames() to list available names.

Attributes:

AlgorithmDictionary

Methods

`GetSolverNames`(*args)	Accessor to the list of solver names.
`addConstraint`(*args)	Adds a constraint to the analysis
`getBounds`()	Accessor to bounds.
`getClassName`()	Accessor to the object's name.
`getErrorMessage`()	Error message accessor.
`getInterestVariables`()	Get the variables to analyse.
`getMaximumAbsoluteError`()	Accessor to maximum allowed absolute error.
`getMaximumConstraintError`()	Accessor to maximum allowed constraint error.
`getMaximumRelativeError`()	Accessor to maximum allowed relative error.
`getMaximumResidualError`()	Accessor to maximum allowed residual error.
`getMinimization`()	Test whether this is a minimization or maximization problem.
`getName`()	Accessor to the object's name.
`getPhysicalModel`()	Physical model accessor.
`getPythonScript`()	Physical model accessor.
`getRawEquations`()	Accessor to constraints equations
`getResult`()	Accessor to optimization result.
`getSolverName`()	Accessor to solver name
`getStartingPoint`()	Accessor to starting point.
`getVariableInputs`()	Accessor to variable input names
`getWarningMessage`()	Warning message accessor.
`hasName`()	Test if the object is named.
`hasValidResult`()	Whether the analysis has been run.
`isReliabilityAnalysis`()	Whether the analysis involves reliability.
`isRunning`()	Whether the analysis is running.
`resetConstraints`()	Clears the current analysis constraints
`run`()	Launch the analysis.
`setBounds`(bounds)	Accessor to bounds.
`setInterestVariables`(variablesNames)	Set the variables to analyse.
`setMaximumAbsoluteError`(maximumAbsoluteError)	Accessor to maximum allowed absolute error.
`setMaximumCallsNumber`(maximumCallsNumber)	Accessor to maximum allowed number of calls.
`setMaximumConstraintError`(maximumConstraintError)	Accessor to maximum allowed constraint error.
`setMaximumRelativeError`(maximumRelativeError)	Accessor to maximum allowed relative error.
`setMaximumResidualError`(maximumResidualError)	Accessor to maximum allowed residual error.
`setMinimization`(minimization)	Tell whether this is a minimization or maximization problem.
`setName`(name)	Accessor to the object's name.
`setSolverName`(name)	Accessor to solver name
`setStartingPoint`(startingPoint)	Accessor to starting point.
`setVariableInputs`(inputs)	Accessor to variable input names

defineProblem
getEqualityConstraints
getInequalityConstraints
getMaximumCallsNumber
getVariablesType
setVariablesType

Examples

>>> import openturns as ot
>>> import persalys

Create the model:

>>> R = persalys.Input('R', 0., ot.LogNormalMuSigma(300., 30., 0.).getDistribution(), 'Yield strength')
>>> F = persalys.Input('F', 0., ot.Normal(75000., 5000.), 'Traction load')
>>> G = persalys.Output('G', 'deviation')
>>> myPhysicalModel = persalys.SymbolicPhysicalModel('myPhysicalModel', [R, F], [G], ['R-F/(pi_*100.0)'])

Create the Model evaluation:

>>> analysis = persalys.OptimizationAnalysis('anAnalysis', myPhysicalModel, 'TNC')
>>> analysis.setInterestVariables(['G'])
>>> analysis.run()

Get the result:

>>> result = analysis.getResult()
>>> x = result.getOptimalPoint()
>>> y = result.getOptimalValue()

__init__(*args)¶

static GetSolverNames(*args)¶

Accessor to the list of solver names.

Returns:

namesDescription: List of available solver names.

addConstraint(*args)¶

Adds a constraint to the analysis

Parameters:

constraintstr: Constraint equation

getBounds()¶

Accessor to bounds.

Returns:

boundsInterval: Problem bounds.

getClassName()¶

Accessor to the object’s name.

Returns:

class_namestr: The object class name (object.__class__.__name__).

getErrorMessage()¶

Error message accessor.

Returns:

messagestr: Error message if the analysis failed

getInterestVariables()¶

Get the variables to analyse.

Returns:

variablesNamessequence of str: Names of the variables to analyse

getMaximumAbsoluteError()¶

Accessor to maximum allowed absolute error.

Returns:

maximumAbsoluteErrorfloat: Maximum allowed absolute error.

getMaximumConstraintError()¶

Accessor to maximum allowed constraint error.

Returns:

maximumConstraintErrorfloat: Maximum allowed constraint error.

getMaximumRelativeError()¶

Accessor to maximum allowed relative error.

Returns:

maximumRelativeErrorfloat: Maximum allowed relative error.

getMaximumResidualError()¶

Accessor to maximum allowed residual error.

Returns:

maximumResidualErrorfloat: Maximum allowed residual error.

getMinimization()¶

Test whether this is a minimization or maximization problem.

Returns:

valuebool: True if this is a minimization problem (default), False otherwise.

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getPhysicalModel()¶

Physical model accessor.

Returns:

modelPhysicalModel: Physical model

getPythonScript()¶

Physical model accessor.

Returns:

scriptstr: Python script to replay the analysis

getRawEquations()¶

Accessor to constraints equations

Returns:

equationssequence of str: Constraints equations

getResult()¶

Accessor to optimization result.

Returns:

resultOptimizationResult: Result class.

getSolverName()¶

Accessor to solver name

Returns:

solverstr: Solver name

getStartingPoint()¶

Accessor to starting point.

Returns:

startingPointPoint: Starting point.

getVariableInputs()¶

Accessor to variable input names

Returns:

inputsDescription: Variable input names. It is set by default to the list of inputs of the model.

getWarningMessage()¶

Warning message accessor.

Returns:

messagestr: Warning message which can appear during the analysis computation

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

hasValidResult()¶

Whether the analysis has been run.

Returns:

hasValidResultbool: Whether the analysis has already been run

isReliabilityAnalysis()¶

Whether the analysis involves reliability.

Returns:

isReliabilityAnalysisbool: Whether the analysis involves a reliability analysis

isRunning()¶

Whether the analysis is running.

Returns:

isRunningbool: Whether the analysis is running

resetConstraints()¶: Clears the current analysis constraints

run()¶: Launch the analysis.

setBounds(bounds)¶

Accessor to bounds.

Parameters:

boundsInterval: Problem bounds.

setInterestVariables(variablesNames)¶

Set the variables to analyse.

Parameters:

variablesNamessequence of str: Names of the variables to analyse

setMaximumAbsoluteError(maximumAbsoluteError)¶

Accessor to maximum allowed absolute error.

Parameters:

maximumAbsoluteErrorfloat: Maximum allowed absolute error.

setMaximumCallsNumber(maximumCallsNumber)¶

Accessor to maximum allowed number of calls.

Parameters:

Nint: Maximum allowed number of calls.

setMaximumConstraintError(maximumConstraintError)¶

Accessor to maximum allowed constraint error.

Parameters:

maximumConstraintErrorfloat: Maximum allowed constraint error.

setMaximumRelativeError(maximumRelativeError)¶

Accessor to maximum allowed relative error.

Parameters:

maximumRelativeErrorfloat: Maximum allowed relative error.

setMaximumResidualError(maximumResidualError)¶

Accessor to maximum allowed residual error.

Parameters:

maximumResidualErrorfloat: Maximum allowed residual error.

setMinimization(minimization)¶

Tell whether this is a minimization or maximization problem.

Parameters:

minimizationbool: True if this is a minimization problem, False otherwise.

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setSolverName(name)¶

Accessor to solver name

Parameters:

solverstr: Solver name. Use GetSolverNames() to list available names.

setStartingPoint(startingPoint)¶

Accessor to starting point.

Parameters:

startingPointsequence of float: Starting point.

setVariableInputs(inputs)¶

Accessor to variable input names

Parameters:

inputssequence of str: Variable input names. It is set by default to the list of inputs of the model.

OptimizationAnalysis¶

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