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# Jacobi A library that implements Jacobi polynomials and Gauss quadrature related operations.

## Notebook

In the notebooks directory there are tow notebooks, as follows, that shows the basic usage of the library.

## Jacobi polynomials

Jacobi polynomials $P_n^{a,b}(x)$ are implemented in function jacobi(x, n, a, b) where x is where you want to calculate the polynomial of degree n with weights a and b.

To calculate the derivative of jacobi polynomials, use function djacobi(x, n, a, b).

When calculating the weights of Gaussian qudrature, it is necessary to determine the zeros of Jacobi polynomials. The function jacobi_zeros(m, a, b) calculates the zeros of a Jacobi polynomial of degree m with weights a and b. Also present is the non allocating function jacobi_zeros!(m, a, b, x).

Legendre polynomials are a special case when a and b are zero. Function legendre(x, n) implements this simpler recurrence relation.

Also available are Chebyshev polynomials and the respective derivatives and zeros:

• chebyshev(x, n) Chebyshev polynomial of the first kind $T_n(x)$.
• chebyshev2(x, n) Chebyshev polynomial of the second kind $U_n(x)$.
• chebyshev_zeros(n) and chebyshev2_zeros(n) Roots of Chebyshev polynomials
• chebyshev_zeros!(n, x) and chebyshev2_zeros!(n, x) Roots of Chebyshev polynomials

Gaussian quadrature is commonly used to evaluate integrals numerically. To evaluate integrals using Gaussian quadrature, a set of nodes and its corresponding weights must be known. In the most basic algorithm, no node can be specified by the user. It is often convenient to include one or both ends of the domain and this provides different quadrature rules.

The different quadrature rules implemented are:

• Gauss-Jacobi, where no nodes are specified (extension gj).
• Gauss-Lobatto-Jacobi, where both ends of the domain are included (extension glj).
• Gauss-Radau-Jacobi, where the node -1 (leftmost end of the domain) is included (extension grjm).
• Gauss-Radau-Jacobi, where the node +1 (rightmost end of the domain) is included (extension grjp).

These formulations implement the numerical integration of $$\int_{-1}^1 (1-x)^a(1+x)^b f(x)dx$$

The functions zgj, zglj, zgrjm and zgrjp calculate the nodes of the quadrature rule. The corresponding weights are calculated using functions wgj, wglj, wgrjm and wgrjp. Of course in both cases, the weights a and bmust be specified.

As an example

fun(x) = sin(x)
z = zglj(5, 0.0, 0.0)
w = wglj(z, 0.0, 0.0)
f = fun(z)
Ix = sum(w .* f)


where Ix is the estimate of the integral.

Gaussian quadrature is useful because it allows the exact integration of polynomials using few nodes (the exact order depends on the quadrature rule cited above).

But there is another advantage: the nodes of the quadrature rule is very convenient for interpolating functions using high order polynomials and is commonly used in high order finite element procedures such as hp-FEM or spectral element method. In these applications, it is necessary to compute derivatives and interpolate data from different grids.

The functions dgj, dglj, dgrjm and drjp calculates the derivative matrix as shown in the example that follows:

fun(x) = sin(x)
z = zglj(5, 0.0, 0.0)
D = dglj(z, 0.0, 0.0)
f = fun(z)
df = D * f


where df is an estimate of the derivative of the function at the quadrature nodes.

Another important operation is interpolation. If a function is known at some nodes, in this case the quadrature nodes, how can we accurately interpolate the function on other nodes? Since we know the nodes, Lagrangian interpolation is the best way. The function lagrange implements the standard definition of the Langrangian interpolation. The example below plots the Lagrangian interpolators of the Gauss-Lobatto-Jacobi quadrature points for 5 nodes.

using PyPlot
Q = 5
z = zglj(Q)
nx = 201
x = -1.0:0.01:1.0
y = zeros(nx, Q)
for k = 1:Q, i=1:nx
y[i,k] = lagrange(k, x[i], z)
end

for k=1:Q
plot(x, y[:,k])
end


If the operation above is to be repeated often, pre-calculating the Lagrangian interpolators is useful and an Interpolation matrix can be calculated. The following example illustrates the use of the interpolation matrix that can be computed with the function interp_mat.

using PyPlot
Q = 5
z = zglj(Q)
nx = 201
x = -1.0:0.01:1.0
ye = sin(pi*z)
ye2 = sin(pi*x)
Im = interp_mat(x, z)
y = Im * ye
plot(z, ye, "o")
plot(x, ye2, "r-")
plot(x, y, "b-")


increasing the number of quadrature points the interpolated function (blue line) becomes more accurate.

## References

This package was implemented using both references below.

• Spectral/hp Element Methods for CFD, 2nd edition, Karniadakis and Sherwin, 2005.
• NIST Handbook of Mathematical Functions (http://dlmf.nist.gov/18)

04/22/2014

5 months ago

95 commits