When creating high degree function spaces, it is important to define the degrees of freedom (DOFs) of a space in a way that leads to well behaved basis functions. For example, if a Lagrange space is created using equally spaced points, then the basis functions with exhibit Runge’s phenomenon and large peaks will be observed near the edges of the cell.

When a finite element is defined using points evaluation DOFs, we can use the Lebesgue constant to indicate how well behaved a set of basis functions will be. The Lebesgue constant is defined by

\[\Lambda = \max_{x\in R}\left(\sum_i\left|\phi_i(x)\right|\right),\]

where \(R\) is the reference cell and \(\phi_0\) to \(\phi_{n-1}\) are the basis functions of the finite element space. A smaller value of \(\Lambda\) indicates a better set of basis functions.

In this demo, we look at how the Lebesgue constant can be approximated using Basix, and how variants of Lagrange elements that have lower Lebesgue constants can be created.

We begin by importing Basix and Numpy.

```
import numpy as np
import basix
from basix import CellType, ElementFamily, LagrangeVariant, LatticeType
```

In this demo, we consider Lagrange elements defined on a triangle. We start by creating a degree 15 Lagrange element that uses equally spaced points. This element will exhibit Runge’s phenomenon, so we expect a large Lebesgue constant.

```
lagrange = basix.create_element(
ElementFamily.P, CellType.triangle, 15, LagrangeVariant.equispaced)
```

To estimate the Lebesgue constant, we create a lattice of points on the triangle and compute

\[\Lambda \approx \max_{x\in L}\left(\sum_i\left|\phi_i(x)\right|\right),\]

where \(L\) is the set of points in our lattice. As \(L\) is a subset of \(R\), the values we compute will be lower bounds of the true Lebesgue constants.

The function create_lattice takes four inputs: the cell type, the number of points in each direction, the lattice type (in this example, we use an equally spaced lattice), and a bool indicating whether or not points on the boundary should be included. We tabulate our element at the points in the lattice then use Numpy to compute the max of the sum.

As expected, the value is large.

```
points = basix.create_lattice(
CellType.triangle, 50, LatticeType.equispaced, True)
tab = lagrange.tabulate(0, points)[0]
print(max(np.sum(np.abs(tab), axis=0)))
```

A Lagrange element with a lower Lebesgue constant can be created by placing the DOFs at Gauss-Lobatto-Legendre (GLL) points. Passing LagrangeVariant.gll_warped into create_element will make an element that places its DOF points at warped GLL points on the triangle, as described in Nodal Discontinuous Galerkin Methods (Hesthaven, Warburton, 2008, pp 175-180).

The Lebesgue constant for this variant of the element is much smaller than for the equally spaced element.

```
gll = basix.create_element(
ElementFamily.P, CellType.triangle, 15, LagrangeVariant.gll_warped)
print(max(np.sum(np.abs(gll.tabulate(0, points)[0]), axis=0)))
```

An even lower Lebesgue constant can be obtained by placing the DOF points at GLL points mapped onto a triangle following the method proposed in Recursive, Parameter-Free, Explicitly Defined Interpolation Nodes for Simplices (Isaac, 2020).

```
gll2 = basix.create_element(
ElementFamily.P, CellType.triangle, 15, LagrangeVariant.gll_isaac)
print(max(np.sum(np.abs(gll2.tabulate(0, points)[0]), axis=0)))
```