IteratedQuadrature¶

class IteratedQuadrature(*args)¶

Multivariate integration algorithm.

Parameters

univariateQuadratureIntegrationAlgorithm: By default, the integration algorithm is the Gauss-Kronrod algorithm (GaussKronrod) with the following parameters: maximumSubIntervals=32, maximumError= $1e-7$ and GKRule = G3K7. Note that the default parametrisation of the GaussKronrod class leads to a more precise evaluation of the integral but the cost is greater. It is recommended to increase the order of the quadratic rule and the number of sub intervals if the integrand or one of the bound functions is smooth but with many oscillations.

Notes

This class enables to approximate the following integral:

$I_f = \int_{a}^{b}\, \int_{l_1(x_0)}^{u_1(x_0)}\, \int_{l_2(x_0, x_1)}^{u_2(x_0,x_1)}\, \int_{l_{n-1}(x_0, \dots, x_{n-2})}^{u_{n-1}(x_0, \dots, x_{n-2})} \, f(x_0, \dots, x_{n-1})\di{x_{n-1}}\dots\di{x_0}$

with $f: \Rset^n \mapsto \Rset^p$ , $l_k, u_k: \Rset^k \mapsto \Rset$ and $n\geq 1$ . For $n=1$ , there is no bound functions $l_k$ and $u_k$ .

Examples

Create an iterated quadrature algorithm:

>>> import openturns as ot
>>> import math as m
>>> a = -m.pi
>>> b = m.pi
>>> f = ot.SymbolicFunction(['x', 'y'], ['1+cos(x)*sin(y)'])
>>> l = [ot.SymbolicFunction(['x'], [' 2+cos(x)'])]
>>> u = [ot.SymbolicFunction(['x'], ['-2-cos(x)'])]

Draw the graph of the integrand and the bounds:

>>> g = ot.Graph('Integration nodes', 'x', 'y', True, 'topright')
>>> g.add(f.draw([a,a],[b,b]))
>>> curve = l[0].draw(a, b).getDrawable(0)
>>> curve.setLineWidth(2)
>>> curve.setColor('red')
>>> g.add(curve)
>>> curve = u[0].draw(a, b).getDrawable(0)
>>> curve.setLineWidth(2)
>>> curve.setColor('red')
>>> g.add(curve)

Evaluate the integral with high precision:

>>> Iref = ot.IteratedQuadrature(ot.GaussKronrod(100000, 1e-13, ot.GaussKronrodRule(ot.GaussKronrodRule.G11K23))).integrate(f, a, b, l, u)

Evaluate the integral with the default GaussKronrod algorithm, and get evaluation points:

>>> f = ot.MemoizeFunction(f)
>>> I1 = ot.IteratedQuadrature(ot.GaussKronrod()).integrate(f, a, b, l, u)
>>> sample1 = f.getInputHistory()
>>> print(I1)
[-25.132...]
>>> n_evals = sample1.getSize()
>>> print(n_evals)
2116
>>> err = abs(100.0*(1.0-I1[0]/Iref[0]))
>>> print(err)
2.2...e-14
>>> cloud = ot.Cloud(sample1)
>>> cloud.setPointStyle('fcircle')
>>> cloud.setColor('green')
>>> g.add(cloud)
>>> f.clearHistory()

Evaluate the integral with the default IteratedQuadrature algorithm:

>>> I2 = ot.IteratedQuadrature().integrate(f, a, b, l, u)
>>> sample2 = f.getInputHistory()
>>> print(I2)
[-25.132...]
>>> n_evals = sample2.getSize()
>>> print(n_evals)
5236
>>> err = abs(100.0*(1.0-I2[0]/Iref[0]))
>>> print(err)
4.6...e-10
>>> cloud = ot.Cloud(sample2)
>>> cloud.setPointStyle('fcircle')
>>> cloud.setColor('gold')
>>> g.add(cloud)

Methods

`getClassName`(self)	Accessor to the object’s name.
`getId`(self)	Accessor to the object’s id.
`getName`(self)	Accessor to the object’s name.
`getShadowedId`(self)	Accessor to the object’s shadowed id.
`getVisibility`(self)	Accessor to the object’s visibility state.
`hasName`(self)	Test if the object is named.
`hasVisibleName`(self)	Test if the object has a distinguishable name.
`integrate`(self, \*args)	Evaluation of the integral of $f$ on a domain.
`setName`(self, name)	Accessor to the object’s name.
`setShadowedId`(self, id)	Accessor to the object’s shadowed id.
`setVisibility`(self, visible)	Accessor to the object’s visibility state.

__init__(self, \*args)¶: Initialize self. See help(type(self)) for accurate signature.

getClassName(self)¶

Accessor to the object’s name.

Returns

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

getId(self)¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getName(self)¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getShadowedId(self)¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getVisibility(self)¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

hasName(self)¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasVisibleName(self)¶

Test if the object has a distinguishable name.

Returns

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

integrate(self, \*args)¶

Evaluation of the integral of $f$ on a domain.

Available usages:

integrate(f, interval)

integrate(f, a, b, lowerBoundFunctions, upperBoundFunctions)

Parameters

fFunction, $f: \Rset^n \mapsto \Rset^p$: The integrand function.
intervalInterval, $interval \in \Rset^n$: The integration domain.
a,bfloat: Bounds of the integration interval of the first scalar input $x_0$
lowerBoundFunctions, upperBoundFunctionslist of Function: List of $n$ functions $(l_0, \dots, l_{n-1})$ and $(u_0, \dots, u_{n-1})$ where $l_k, u_k: \Rset^k \mapsto \Rset$ defining the integration domain as defined above. The bound functions can cross each other.

Returns