Plot Smolyak multi-indices

The goal of this example is to plot the multi-indices used in Smolyak’s quadrature.

For a given dimension d_x \in \mathbb{N} and a given level \ell \in \mathbb{N} Smolyak’s quadrature is the combination of tensorized univariate quadratures. These quadrature are defined by the set of multi-indices:

\mathcal{S}_{\ell, d_x} = \left\{\|\boldsymbol{k}\|_1 \leq \ell
                                 + d_x - 1\right\}

where \|\boldsymbol{k}\|_1 = k_1 + ... + k_{d_x} is the 1-norm of the multi-index \boldsymbol{k} \in \mathbb{N}^{d_x}.

The goal of this script is to plot the multi-indices involved in Smolyak’s quadrature for different values of the level \ell in dimension d_x = 2.

import openturns as ot
import openturns.experimental as otexp
import openturns.viewer as otv
from matplotlib import pylab as plt

In the first example, we print the indices involved in Smolyak-Legendre quadrature of level 3. The multi-indices are computed using the computeCombination() method. Actually, the multi-indices do not actually depend on the underlying univariate quadratures, but this is required for the SmolyakExperiment class.

collection = [ot.GaussProductExperiment()] * 2
level = 3
print("level = ", level)
experiment = otexp.SmolyakExperiment(collection, level)
indices = experiment.computeCombination()
level =  3

We see that the multi-indices have a sum which is equal to either 3 or 4. In other words, these multi-indices belong to two different layers of constant 1-norms.

In order to see how this evolves depending on the level of the quadrature, the following function creates a 2D plot of the set of multi-indices.

def drawSmolyakIndices(level):
    # Plot Smolyak indices of given level in 2 dimensions
    collection = [ot.GaussProductExperiment()] * 2
    experiment = otexp.SmolyakExperiment(collection, level)
    indices = experiment.computeCombination()
    sample = indices
    graph = ot.Graph("L = %d" % (level), "k1", "k2", True)
    cloud = ot.Cloud(sample)
    return graph

In the following script, we create a grid of plots, where each graph corresponds to a given quadrature level. The bounding box of each graph is set to a constant value, so that all graphs have the same X and Y bounds.

levelMax = 8.0
boundingBox = ot.Interval([0.0] * 2, [levelMax] * 2)
nbrows = 2
nbcols = 3
grid = ot.GridLayout(nbrows, nbcols)
level = 1
for i in range(nbrows):
    for j in range(nbcols):
        graph = drawSmolyakIndices(level)
        if i < nbrows - 1:
        if j > 0:
        grid.setGraph(i, j, graph)
        level += 1
view = otv.View(grid, figure_kw={"figsize": (5.0, 3.0)})
plt.subplots_adjust(wspace=0.5, hspace=0.7)
, L = 1, L = 2, L = 3, L = 4, L = 5, L = 6

We see that when the level increases, the set of Smolyak multi-indices correspond to two different layers of constant 1-norm. This is a consequence of Smolyak’s quadrature, which is based on tensorization of univariate difference quadratures.