EfficientGlobalOptimization¶

class EfficientGlobalOptimization(*args)¶

Efficient Global Optimization algorithm.

The EGO algorithm [jones1998] is an adaptative optimization method based on kriging. An initial design of experiment is used to build a first metamodel. At each iteration a new point that maximizes a criterion is chosen as optimizer candidate. The criterion uses a tradeoff between the metamodel value and the conditional variance. Then the new point is evaluated using the original model and the metamodel is relearnt on the extended design of experiment.

Available constructors:: EfficientGlobalOptimization(problem, krigingResult)

Parameters

problemOptimizationProblem: The optimization problem to solve optionally, a 2nd objective marginal can be used as noise
krigingResultKrigingResult: The result of the meta-model on the first design of experiment

Notes

Each point added to the metamodel design seeks to improve the current minimum. We chose the point so as to maximize an improvement criterion based on the metamodel.

$I(x_{new}) = max(f_{min} - Y_{new}, 0)$

The default criteria is called EI (Expected Improvement) and aims at maximizing the mean improvement:

$\mathbb{E}\left[I(x_{new})\right] = \mathbb{E}\left[max(f_{min} - Y_{new}, 0)\right]$

This criterion is explicited using the kriging mean and variance:

$\mathbb{E}\left[I(x_{new})\right] = (f_{min} - m_K(x_{new})) \Phi\left( \frac{f_{min} - m_K(x_{new})}{s_K(x_{new})} \right) + s_K(x_{new}) \phi\left( \frac{f_{min} - m_K(x_{new})}{s_K(x_{new})} \right)$

An observation noise variance can be provided thanks to a 2nd objective marginal.

$Y_{obs} = Y(x) + \sigma_{\epsilon}(x) \epsilon$

In that case the AEI (Augmented Expected Improvement) formulation is used. As we don’t have access to the real minimum of the function anymore a quantile of the kriging prediction is used, with the constant $c$ :

$u(x) = m_K(x) + c s_K(x)$

This criterion is minimized over the design points:

$x_{min} = \argmax_{x_i} (u(x_i))$

The AEI criterion reads:

$AEI(x_{new}) = \mathbb{E}\left[max(m_K(x_{min}) - Y_{new}, 0)\right] \times \left(1 - \frac{\sigma_{\epsilon}(x_{new})}{\sqrt{\sigma_{\epsilon}^2(x_{new})+s^2_K(x_{new})}} \right)$

with

$\mathbb{E}\left[max(m_K(x_{min}) - Y_{new}, 0)\right] = (m_K(x_{min}) - m_K(x_{new})) \Phi\left( \frac{m_K(x_{min}) - m_K(x_{new})}{s_K(x_{new})} \right) + s_K(x_{new}) \phi\left( \frac{m_K(x_{min}) - m_K(x_{new})}{s_K(x_{new})} \right)$

A less computationally expensive noise function can be provided through setNoiseModel() to evaluate $\sigma^2_{\epsilon}(x)$ for the improvement criterion optimization, the objective being only used to compute values and associated noise at design points.

By default the criteria is minimized using MultiStart with starting points uniformly sampled in the optimization problem bounds, see setMultiStartExperimentSize() and setMultiStartNumber(). This behavior can be overridden by using another solver with setOptimizationAlgorithm().

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> dim = 4
>>> model = ot.SymbolicFunction(['x1', 'x2', 'x3', 'x4'],
...     ['x1*x1+x2^3*x1+x3+x4'])
>>> model = ot.MemoizeFunction(model)
>>> bounds = ot.Interval([-5.0] * dim, [5.0] * dim)
>>> problem = ot.OptimizationProblem()
>>> problem.setObjective(model)
>>> problem.setBounds(bounds)
>>> experiment = ot.Composite([0.0] * dim, [1.0, 2.0, 4.0])
>>> inputSample = experiment.generate()
>>> outputSample = model(inputSample)
>>> print('Initial minimum output value: ', outputSample.getMin())
Initial minimum output value:  [-248]
>>> covarianceModel = ot.SquaredExponential([2.0] * dim, [0.1])
>>> basis = ot.ConstantBasisFactory(dim).build()
>>> kriging = ot.KrigingAlgorithm(inputSample, outputSample, covarianceModel, basis)
>>> kriging.run()
>>> algo = ot.EfficientGlobalOptimization(problem, kriging.getResult())
>>> algo.setMaximumEvaluationNumber(2)
>>> algo.run()
>>> result = algo.getResult()
>>> updatedKrigingResult = algo.getKrigingResult()
>>> updatedOutputSample = updatedKrigingResult.getOutputSample()
>>> print('Updated minimum output value: ', updatedOutputSample.getMin())
Updated minimum output value:  [-610]

Methods

`getAEITradeoff`()	AEI tradeoff constant accessor.
`getClassName`()	Accessor to the object's name.
`getCorrelationLengthFactor`()	Correlation length stopping criterion factor accessor.
`getExpectedImprovement`()	Expected improvement values.
`getId`()	Accessor to the object's id.
`getImprovementFactor`()	Improvement criterion factor accessor.
`getKrigingResult`()	Retrieve the Kriging result.
`getMaximumAbsoluteError`()	Accessor to maximum allowed absolute error.
`getMaximumConstraintError`()	Accessor to maximum allowed constraint error.
`getMaximumEvaluationNumber`()	Accessor to maximum allowed number of evaluations.
`getMaximumIterationNumber`()	Accessor to maximum allowed number of iterations.
`getMaximumRelativeError`()	Accessor to maximum allowed relative error.
`getMaximumResidualError`()	Accessor to maximum allowed residual error.
`getMultiStartExperimentSize`()	Size of the design to draw starting points.
`getMultiStartNumber`()	Number of starting points for the criterion optimization.
`getName`()	Accessor to the object's name.
`getNoiseModel`()	Improvement noise model accessor.
`getOptimizationAlgorithm`()	Expected improvement solver accessor.
`getParameterEstimationPeriod`()	Parameter estimation period accessor.
`getProblem`()	Accessor to optimization problem.
`getResult`()	Accessor to optimization result.
`getShadowedId`()	Accessor to the object's shadowed id.
`getStartingPoint`()	Accessor to starting point.
`getVerbose`()	Accessor to the verbosity flag.
`getVisibility`()	Accessor to the object's visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`run`()	Launch the optimization.
`setAEITradeoff`(c)	AEI tradeoff constant accessor.
`setCorrelationLengthFactor`(b)	Correlation length stopping criterion factor accessor.
`setImprovementFactor`(improvementFactor)	Improvement criterion factor accessor.
`setMaximumAbsoluteError`(maximumAbsoluteError)	Accessor to maximum allowed absolute error.
`setMaximumConstraintError`(maximumConstraintError)	Accessor to maximum allowed constraint error.
`setMaximumEvaluationNumber`(...)	Accessor to maximum allowed number of evaluations.
`setMaximumIterationNumber`(maximumIterationNumber)	Accessor to maximum allowed number of iterations.
`setMaximumRelativeError`(maximumRelativeError)	Accessor to maximum allowed relative error.
`setMaximumResidualError`(maximumResidualError)	Accessor to maximum allowed residual error.
`setMultiStartExperimentSize`(...)	Size of the design to draw starting points.
`setMultiStartNumber`(multiStartNumberSize)	Number of starting points for the criterion optimization.
`setName`(name)	Accessor to the object's name.
`setNoiseModel`(noiseModel)	Improvement noise model accessor.
`setOptimizationAlgorithm`(solver)	Expected improvement solver accessor.
`setParameterEstimationPeriod`(...)	Parameter estimation period accessor.
`setProblem`(problem)	Accessor to optimization problem.
`setProgressCallback`(*args)	Set up a progress callback.
`setResult`(result)	Accessor to optimization result.
`setShadowedId`(id)	Accessor to the object's shadowed id.
`setStartingPoint`(startingPoint)	Accessor to starting point.
`setStopCallback`(*args)	Set up a stop callback.
`setVerbose`(verbose)	Accessor to the verbosity flag.
`setVisibility`(visible)	Accessor to the object's visibility state.

__init__(*args)¶

getAEITradeoff()¶

AEI tradeoff constant accessor.

Returns

cfloat: Used to define a quantile of the kriging prediction at the design points. $u(x)=m_K(x)+c*s_K(x)$

getClassName()¶

Accessor to the object’s name.

Returns

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

getCorrelationLengthFactor()¶

Correlation length stopping criterion factor accessor.

When a correlation length becomes smaller than the minimal distance between design point for a single component that means the model tends to be noisy, and the EGO formulation is not adapted anymore.

Returns

bfloat: Used to define a stopping criterion on the minimum correlation length: $\theta_i < \frac{\Delta_i^{min}}{b}$ with $\Delta^{min}$ the minimum distance between design points.

getExpectedImprovement()¶

Expected improvement values.

Returns

eiSample: The expected improvement optimal values.

getId()¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getImprovementFactor()¶

Improvement criterion factor accessor.

Returns

alphapositive float, default=0.0 (disables the criterion): Used to define a stopping criterion on the improvement criterion: $I_{max} < \alpha |Y_{min}|$ with $I_{max}$ the current maximum of the improvement and $Y_{min}$ the current optimum.

getKrigingResult()¶

Retrieve the Kriging result.

Returns

krigingResultKrigingResult: Kriging result that takes all observations into account.

Notes

Before run() is called, this method returns the KrigingResult passed to the constructor. Once run() has been called, it returns an updated KrigingResult that takes new observations into account.

getMaximumAbsoluteError()¶

Accessor to maximum allowed absolute error.

Returns

maximumAbsoluteErrorfloat: Maximum allowed absolute error, where the absolute error is defined by $\epsilon^a_n=\|\vect{x}_{n+1}-\vect{x}_n\|_{\infty}$ where $\vect{x}_{n+1}$ and $\vect{x}_n$ are two consecutive approximations of the optimum.

getMaximumConstraintError()¶

Accessor to maximum allowed constraint error.

Returns

maximumConstraintErrorfloat: Maximum allowed constraint error, where the constraint error is defined by $\gamma_n=\|g(\vect{x}_n)\|_{\infty}$ where $\vect{x}_n$ is the current approximation of the optimum and $g$ is the function that gathers all the equality and inequality constraints (violated values only)

getMaximumEvaluationNumber()¶

Accessor to maximum allowed number of evaluations.

Returns

Nint: Maximum allowed number of evaluations.

getMaximumIterationNumber()¶

Accessor to maximum allowed number of iterations.

Returns

Nint: Maximum allowed number of iterations.

getMaximumRelativeError()¶

Accessor to maximum allowed relative error.

Returns

maximumRelativeErrorfloat: Maximum allowed relative error, where the relative error is defined by $\epsilon^r_n=\epsilon^a_n/\|\vect{x}_{n+1}\|_{\infty}$ if $\|\vect{x}_{n+1}\|_{\infty}\neq 0$ , else $\epsilon^r_n=-1$ .

getMaximumResidualError()¶

Accessor to maximum allowed residual error.

Returns

maximumResidualErrorfloat: Maximum allowed residual error, where the residual error is defined by $\epsilon^r_n=\frac{\|f(\vect{x}_{n+1})-f(\vect{x}_{n})\|}{\|f(\vect{x}_{n+1})\|}$ if $\|f(\vect{x}_{n+1})\|\neq 0$ , else $\epsilon^r_n=-1$ .

getMultiStartExperimentSize()¶

Size of the design to draw starting points.

Returns

multiStartExperimentSizeint: The size of the Monte Carlo design from which to select the best starting points.

getMultiStartNumber()¶

Number of starting points for the criterion optimization.

Returns

multiStartNumberint: The number of starting points for the criterion optimization.

getName()¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getNoiseModel()¶

Improvement noise model accessor.

Returns

noiseVarianceFunction: The noise variance $\sigma^2_{\epsilon}(x)$ used for the AEI criterion optimization only. Of same input dimension than the objective and 1-d output.

getOptimizationAlgorithm()¶

Expected improvement solver accessor.

Returns

solverOptimizationSolver: The solver used to optimize the expected improvement

getParameterEstimationPeriod()¶

Parameter estimation period accessor.

Returns

periodint: The number of iterations between covariance parameters re-learn. Default is 1 (each iteration). Can be set to 0 (never).

getProblem()¶

Accessor to optimization problem.

Returns

problemOptimizationProblem: Optimization problem.

getResult()¶

Accessor to optimization result.

Returns

resultOptimizationResult: Result class.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getStartingPoint()¶

Accessor to starting point.

Returns

startingPointPoint: Starting point.

getVerbose()¶

Accessor to the verbosity flag.

Returns

verbosebool: Verbosity flag state.

getVisibility()¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

hasName()¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool: True if the name is not empty and not the default one.

run()¶: Launch the optimization.

setAEITradeoff(c)¶

AEI tradeoff constant accessor.

Parameters

cfloat: Used to define a quantile of the kriging prediction at the design points. $u(x)=m_K(x)+c*s_K(x)$

setCorrelationLengthFactor(b)¶

Correlation length stopping criterion factor accessor.

When a correlation length becomes smaller than the minimal distance between design point for a single component that means the model tends to be noisy, and the EGO formulation is not adapted anymore.

Parameters

bfloat: Used to define a stopping criterion on the minimum correlation length: $\theta_i < \frac{\Delta_i^{min}}{b}$ with $\Delta^{min}$ the minimum distance between design points.

setImprovementFactor(improvementFactor)¶

Improvement criterion factor accessor.

Parameters

alphapositive float, default=0.0 (disables the criterion): Used to define a stopping criterion on the improvement criterion: $I_{max} < \alpha |Y_{min}|$ with $I_{max}$ the current maximum of the improvement and $Y_{min}$ the current optimum.

setMaximumAbsoluteError(maximumAbsoluteError)¶

Accessor to maximum allowed absolute error.

Parameters

maximumAbsoluteErrorfloat: Maximum allowed absolute error, where the absolute error is defined by $\epsilon^a_n=\|\vect{x}_{n+1}-\vect{x}_n\|_{\infty}$ where $\vect{x}_{n+1}$ and $\vect{x}_n$ are two consecutive approximations of the optimum.

setMaximumConstraintError(maximumConstraintError)¶

Accessor to maximum allowed constraint error.

Parameters

maximumConstraintErrorfloat: Maximum allowed constraint error, where the constraint error is defined by $\gamma_n=\|g(\vect{x}_n)\|_{\infty}$ where $\vect{x}_n$ is the current approximation of the optimum and $g$ is the function that gathers all the equality and inequality constraints (violated values only)

setMaximumEvaluationNumber(maximumEvaluationNumber)¶

Accessor to maximum allowed number of evaluations.

Parameters

Nint: Maximum allowed number of evaluations.

setMaximumIterationNumber(maximumIterationNumber)¶

Accessor to maximum allowed number of iterations.

Parameters

Nint: Maximum allowed number of iterations.

setMaximumRelativeError(maximumRelativeError)¶

Accessor to maximum allowed relative error.

Parameters

maximumRelativeErrorfloat: Maximum allowed relative error, where the relative error is defined by $\epsilon^r_n=\epsilon^a_n/\|\vect{x}_{n+1}\|_{\infty}$ if $\|\vect{x}_{n+1}\|_{\infty}\neq 0$ , else $\epsilon^r_n=-1$ .

setMaximumResidualError(maximumResidualError)¶

Accessor to maximum allowed residual error.

Parameters

Maximum allowed residual error, where the residual error is defined by: $\epsilon^r_n=\frac{\|f(\vect{x}_{n+1})-f(\vect{x}_{n})\|}{\|f(\vect{x}_{n+1})\|}$ if $\|f(\vect{x}_{n+1})\|\neq 0$ , else $\epsilon^r_n=-1$ .

setMultiStartExperimentSize(multiStartExperimentSize)¶

Size of the design to draw starting points.

Parameters

multiStartExperimentSizeint: The size of the Monte Carlo design from which to select the best starting points. The default number can be tweaked with the EfficientGlobalOptimization-DefaultMultiStartExperimentSize key from ResourceMap.

setMultiStartNumber(multiStartNumberSize)¶

Number of starting points for the criterion optimization.

Parameters

multiStartNumberint: The number of starting points for the criterion optimization. The default number can be tweaked with the EfficientGlobalOptimization-DefaultMultiStartNumber key from ResourceMap.

setName(name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setNoiseModel(noiseModel)¶

Improvement noise model accessor.

Parameters

noiseVarianceFunction: The noise variance $\sigma^2_{\epsilon}(x)$ used for the AEI criterion optimization only. Of same input dimension than the objective and 1-d output.

setOptimizationAlgorithm(solver)¶

Expected improvement solver accessor.

Parameters

solverOptimizationSolver: The solver used to optimize the expected improvement

setParameterEstimationPeriod(parameterEstimationPeriod)¶

Parameter estimation period accessor.

Parameters

periodint: The number of iterations between covariance parameters re-learn. Default is 1 (each iteration). Can be set to 0 (never). The default number can be tweaked with the EfficientGlobalOptimization-DefaultParameterEstimationPeriod key from ResourceMap.

setProblem(problem)¶

Accessor to optimization problem.

Parameters

problemOptimizationProblem: Optimization problem.

setProgressCallback(*args)¶

Set up a progress callback.

Can be used to programmatically report the progress of an optimization.

Parameters

callbackcallable: Takes a float as argument as percentage of progress.

Examples

>>> import sys
>>> import openturns as ot
>>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2'])
>>> problem = ot.OptimizationProblem(rosenbrock)
>>> solver = ot.OptimizationAlgorithm(problem)
>>> solver.setStartingPoint([0, 0])
>>> solver.setMaximumResidualError(1.e-3)
>>> solver.setMaximumEvaluationNumber(10000)
>>> def report_progress(progress):
...     sys.stderr.write('-- progress=' + str(progress) + '%\n')
>>> solver.setProgressCallback(report_progress)
>>> solver.run()

setResult(result)¶

Accessor to optimization result.

Parameters

resultOptimizationResult: Result class.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters

idint: Internal unique identifier.

setStartingPoint(startingPoint)¶

Accessor to starting point.

Parameters

startingPointPoint: Starting point.

setStopCallback(*args)¶

Set up a stop callback.

Can be used to programmatically stop an optimization.

Parameters

callbackcallable: Returns an int deciding whether to stop or continue.

Examples

>>> import openturns as ot
>>> rosenbrock = ot.SymbolicFunction(['x1', 'x2'], ['(1-x1)^2+100*(x2-x1^2)^2'])
>>> problem = ot.OptimizationProblem(rosenbrock)
>>> solver = ot.OptimizationAlgorithm(problem)
>>> solver.setStartingPoint([0, 0])
>>> solver.setMaximumResidualError(1.e-3)
>>> solver.setMaximumEvaluationNumber(10000)
>>> def ask_stop():
...     return True
>>> solver.setStopCallback(ask_stop)
>>> solver.run()

setVerbose(verbose)¶

Accessor to the verbosity flag.

Parameters

verbosebool: Verbosity flag state.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters

visiblebool: Visibility flag.

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