A Julia package to compute `n`

-point Gauss quadrature nodes and weights to 16-digit accuracy and in `O(n)`

time. So far the package includes `gausschebyshev()`

, `gausslegendre()`

, `gaussjacobi()`

, `gaussradau()`

, `gausslobatto()`

, `gausslaguerre()`

, and `gausshermite()`

. This package is heavily influenced by Chebfun.

An introduction to Gauss quadrature can be found here. For a quirky account on the history of computing Gauss-Legendre quadrature, see [6].

The fastest Julia code for Gauss quadrature nodes and weights (without tabulation).

Change the perception that Gauss quadrature rules are expensive to compute.

Here we compute `100000`

nodes and weights of the Gauss rules. Try a million or ten million.

```
@time gausschebyshev( 100000 );
0.002681 seconds (9 allocations: 1.526 MB, 228.45% gc time)
@time gausslegendre( 100000 );
0.007110 seconds (17 allocations: 2.671 MB)
@time gaussjacobi( 100000, .9, -.1 );
1.782347 seconds (20.84 k allocations: 1.611 GB, 22.89% gc time)
@time gaussradau( 100000 );
1.849520 seconds (741.84 k allocations: 1.625 GB, 22.59% gc time)
@time gausslobatto( 100000 );
1.905083 seconds (819.73 k allocations: 1.626 GB, 23.45% gc time)
@time gausslaguerre( 100000 )
.891567 seconds (115.19 M allocations: 3.540 GB, 3.05% gc time)
@time gausshermite( 100000 );
0.249756 seconds (201.22 k allocations: 131.643 MB, 4.92% gc time)
```

The paper [1] computed a billion Gauss-Legendre nodes. So here we will do a billion + 1.

```
@time gausslegendre( 1000000001 );
131.392154 seconds (17 allocations: 26.077 GB, 1.17% gc time)
```

(The nodes near the endpoints coalesce in 16-digits of precision.)

There are four kinds of Gauss-Chebyshev quadrature rules, corresponding to four weight functions:

1st kind, weight function

`w(x) = 1/sqrt(1-x^2)`

2nd kind, weight function

`w(x) = sqrt(1-x^2)`

3rd kind, weight function

`w(x) = sqrt((1+x)/(1-x))`

4th kind, weight function

`w(x) = sqrt((1-x)/(1+x))`

They are all have explicit simple formulas for the nodes and weights [4].

Gauss quadrature for the weight function `w(x) = 1`

.

For

`n<=5`

: Use an analytic expression.For

`n<=60`

: Use Newton's method to solve`Pn(x)=0`

. Evaluate`Pn`

and`Pn'`

by 3-term recurrence. Weights are related to`Pn'`

.For

`n>60`

: Use asymptotic expansions for the Legendre nodes and weights [1].

Gauss quadrature for the weight functions `w(x) = (1-x)^a(1+x)^b`

, `a,b>-1`

.

For

`n<=100`

: Use Newton's method to solve`Pn(x)=0`

. Evaluate`Pn`

and`Pn'`

by three-term recurrence.For

`n>100`

: Use Newton's method to solve`Pn(x)=0`

. Evaluate`Pn`

and`Pn'`

by an asymptotic expansion (in the interior of`[-1,1]`

) and the three-term recurrence`O(n^-2)`

close to the endpoints. (This is a small modification to the algorithm described in [3].)For

`max(a,b)>5`

: Use the Golub-Welsch algorithm requiring`O(n^2)`

operations.

Gauss quadrature for the weight function `w(x)=1`

, except the endpoint `-1`

is included as a quadrature node.

The Gauss-Radau nodes and weights can be computed via the `(0,1)`

Gauss-Jacobi nodes and weights [3].

Gauss quadrature for the weight function `w(x)=1`

, except the endpoints `-1`

and `1`

are included as nodes.

The Gauss-Lobatto nodes and weights can be computed via the `(1,1)`

Gauss-Jacobi nodes and weights [3].

Gauss quadrature for the weight function `w(x) = exp(-x)`

on `[0,Inf)`

For

`n<128`

: Use the Golub-Welsch algorithm.For

`method=GLR`

: Use the Glaser-Lui-Rohklin algorithm. Evaluate`Ln`

and`Ln'`

by using Taylor series expansions near roots generated by solving the second-order differential equation that`Ln`

satisfies, see [2].For

`n>=128`

: Use a Newton procedure on Riemann-Hilbert asymptotics of Laguerre polynomials, see [5], based on [8]. There are some heuristics to decide which expression to use, it allows a general weight`w(x) = x^alpha exp(-q_m x^m)`

and this is O(sqrt(n)) when allowed to stop when the weights are below the smallest positive floating point number.

Gauss quadrature for the weight function `w(x) = exp(-x^2)`

on the real line.

For

`n<200`

: Use Newton's method to solve`Hn(x)=0`

. Evaluate`Hn`

and`Hn'`

by three-term recurrence.For

`n>=200`

: Use Newton's method to solve`Hn(x)=0`

. Evaluate`Hn`

and`Hn'`

by a uniform asymptotic expansion, see [7]. * The paper [7] also derives an`O(n)`

algorithm for generalized Gauss-Hermite nodes and weights associated to weight functions of the form`exp(-V(x))`

, where`V(x)`

is a real polynomial.

```
@time nodes, weights = gausslegendre( 100000 );
0.007890 seconds (19 allocations: 2.671 MB)
# integrates f(x) = x^2 from -1 to 1
@time dot( weights, nodes.^2 )
0.004264 seconds (7 allocations: 781.484 KB)
0.666666666666666
```

[1] I. Bogaert, "Iteration-free computation of Gauss-Legendre quadrature nodes and weights", SIAM J. Sci. Comput., 36(3), A1008-A1026, 2014.

[2] A. Glaser, X. Liu, and V. Rokhlin. "A fast algorithm for the calculation of the roots of special functions." SIAM J. Sci. Comput., 29 (2007), 1420-1438.

[3] N. Hale and A. Townsend, "Fast and accurate computation of Gauss-Legendre and Gauss-Jacobi quadrature nodes and weights", SIAM J. Sci. Comput., 2012.

[4] J. C. Mason and D. C. Handscomb, "Chebyshev Polynomials", CRC Press, 2002.

[5] P. Opsomer, (in preparation).

[6] A. Townsend, The race for high order Gauss-Legendre quadrature, in SIAM News, March 2015.

[7] A. Townsend, T. Trogdon, and S. Olver, "Fast computation of Gauss quadrature nodes and weights on the whole real line", to appear in IMA Numer. Anal., 2014.

[8] M. Vanlessen, "Strong asymptotics of Laguerre-Type orthogonal polynomials and applications in Random Matrix Theory", Constr. Approx., 25:125-175, 2007.

08/31/2014

11 days ago

227 commits