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Gaussian quadrature — Examples

1 · Prerequisites

2 · Summary

The examples compute the two-node Gauss–Legendre rule from its moments and Jacobi matrix, derive positive two-node weights from cardinal-polynomial squares, and count the nodes and exactness range of a four-coordinate tensor rule. The Legendre example states its countable-choice assumption where the library's Lebesgue measure construction requires it.

3 · Logical flowchart

4 · Definitions, theorems and proofs

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Two Gaussian weights are the squared norms of the two cardinal polynomials

Example

Let (a,b,2,3,μ) be Gaussian moment data, with distinct Gaussian nodes x1<x2 and weights w1,w2. Its cardinal polynomials are ℓ1(X)=X−x2x1−x2,ℓ2(X)=X−x1x2−x1. The degree-three exactness theorem implies w1=∫ℓ12 dμ=μ2−2x2μ1+x22μ0(x1−x2)2>0,w2=∫ℓ22 dμ=μ2−2x1μ1+x12μ0(x2−x1)2>0, where μj=∫xj dμ for j=0,1,2.

Facts & Assumptions

Given: Moment data of order 3, its two-node Gaussian rule, and its distinct nodes x1,x2.

[F1]

The m-node Gaussian quadrature rule for a positive measure: wi=∫ℓi dμ and Q2(f)=w1f(x1)+w2f(x2).

[F2]

Gaussian quadrature is exact through degree 2m−1 and has positive weights: the rule is exact through degree 3 and wi>0.

[F3]

Cardinal-polynomial interpolation and Cardinal interpolation functions are polynomials of the stated degrees: the displayed ℓi are degree one and satisfy ℓi(xk)=1 for k=i and 0 otherwise.

[F4]

Positive-measure Gaussian quadrature moment data and The Lebesgue integral is linear on L1(μ): for j=0,1,2, ∫xj dμ=μj, and integration distributes over the finite polynomial expansion.

Proof

1.1F1F2F3

Since deg⁡(ℓi2)=2, exactness [F2] gives Q2(ℓi2)=∫ℓi2 dμ. Expanding the finite rule sum [F1] and using the cardinal values [F3] gives Q2(ℓi2)=wi, hence ∫ℓi2 dμ=wi.

2.1F2F4step 1.1algebra∎

The formula for ℓ1 gives ℓ1(x)2=(x2−2x2x+x22)/(x1−x2)2. Integrating term by term using [F4] proves the displayed expression for w1. Replacing the omitted node x2 by x1 gives the displayed expression for w2. Their strict positivity is [F2].

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Four two-node Gaussian rules give a sixteen-node tensor rule

Example

Take four coordinate moment data sets, each with a two-node Gaussian rule Qj(f)=wj,1f(xj,1)+wj,2f(xj,2). The tensor rule has node set {(x1,i1,x2,i2,x3,i3,x4,i4):ij∈{1,2}}, so it has 24=16 distinct nodes. It is exact for every coordinatewise polynomial P with degree at most three in each variable. In particular, Q⊗(x13x22x3x40)=∏j=14(∑i=12wj,ixj,iαj)=∏j=14∫xαj dμj(x),(α1,α2,α3,α4)=(3,2,1,0).

Facts & Assumptions

Given: Four order-three moment data sets and their two-node Gaussian rules.

[F1]

Tensor-product Gaussian quadrature: the tensor nodes are all coordinate tuples and the tensor weights are products of the coordinate weights.

[F2]

Coordinatewise product-moment functional for tensor polynomials: the product-moment functional on a coordinate monomial is the product of its coordinate moments.

[F3]

Tensor-product Gaussian quadrature preserves coordinatewise exactness: the tensor rule is exact for the stated coordinatewise degree ranges and has the product number of distinct nodes.

[F4]

Gaussian quadrature is exact through degree 2m−1 and has positive weights: each coordinate rule integrates every degree-at-most-three monomial exactly.

[F5]

The Gauss orthogonal polynomial has simple roots inside the interval: each two-node Gaussian rule has distinct nodes.

Proof

1.1F1construct

Since each coordinate has two distinct nodes by [F5], the sixteen coordinate tuples are distinct: two different tuples differ in a coordinate whose node entries are distinct.

2.1F1F2F3F4step 1.1algebra∎

The node-count statement of [F3] gives 2⋅2⋅2⋅2=16. For each coordinate degree αj≤3, [F4] evaluates the corresponding one-dimensional sum as its moment. Distributing the finite sums in [F1] therefore gives the product of those four moments, which is [F2]. Taking the specified exponents proves the displayed calculation and the general coordinatewise exactness claim.

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The two-node Gauss–Legendre rule is exact through degree three

Example

Assume the Axiom of Countable Choice and take Lebesgue measure restricted to the relative Borel sets of [−1,1]. Its relative support is all of [−1,1], so it gives order-three moment data for m=2. The moments are μ0=2, μ1=0, μ2=2/3, and μ3=0. The monic orthogonal polynomials through degree two are p0=1, p1=X, and p2=X2−1/3. Thus the Gaussian nodes are x1=−1/3 and x2=1/3.

The Jacobi matrix is J2=(01/31/30). Its unit eigenvectors (1,−1)/2 and (1,1)/2 give weights w1=w2=μ0/2=1. Therefore Q2(f)=f(−1/3)+f(1/3) is exact for every real polynomial of degree at most three.

Facts & Assumptions

Given: Countable choice and Lebesgue measure on [−1,1] restricted to relative Borel sets.

[A1]

The Axiom of Countable Choice (ACω): the Axiom of Countable Choice is assumed for the Lebesgue measure and Riemann–Lebesgue results used in this example.

[F2]

A box in Rn with parameters ai≤bi is Lebesgue measurable of measure ∏i<n(bi−ai), whichever of its faces are included: a closed interval has measure its length; every positive-length subinterval of [−1,1] has positive measure.

[F4]

Monic orthogonal polynomials exist uniquely through the quadrature order: checking the orthogonality equations identifies the monic polynomials uniquely.

[F5]

Monic orthogonal polynomials satisfy a three-term recurrence: the normalized recurrence coefficients form the displayed symmetric Jacobi matrix.

[F6]

The Jacobi matrix spectrum is the set of Gauss nodes: its eigenvalues are the Gaussian nodes.

[F7]

Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square: a unit eigenvector at a node gives weight μ0 times the square of its first coordinate.

[F8]
[F10]

The Borel sigma-algebra of a subspace is the trace of the ambient Borel sigma-algebra: every relative Borel set of [−1,1] is B∩[−1,1] for an ambient real Borel set B.

[F11]

Nonnegative real square roots exist uniquely: every positive real has a unique positive square root.

Proof

1.1A1F1F2F9F10choosealgebra

By [F10], every relative Borel set is B∩[−1,1] for ambient Borel B. The interval is closed, hence ambient Borel, so this intersection is ambient Borel and is Lebesgue measurable by [F1]. Restricting Lebesgue measure to this sigma-algebra therefore defines the asserted positive measure; disjoint relative Borel unions are the same ambient measurable unions, so countable additivity is inherited. For x∈[−1,1], [F9] gives an ε>0 such that (x−ε,x+ε)∩[−1,1] lies in any chosen relative open neighborhood U of x. Choose a closed interval I of positive length inside that intersection, using a one-sided interval when x is an endpoint. Then I is relative Borel and [F2] gives μ(I)>0. Since U=I⊔(U∖I) and both sets are relative Borel, countable additivity and nonnegativity from [F1] imply μ(U)≥μ(I)>0. Thus every x lies in the relative support, which has at least three points.

1.2A1F3algebra

The functions 1,x,x2,x3 are continuous, hence Riemann integrable by [F3]. Their primitives x,x2/2,x3/3,x4/4, evaluated at −1 and 1, give respectively 2,0,2/3,0. The Riemann–Lebesgue equality in [F3] gives the stated moment values.

2.1F3F4F11step 1.2algebra

The monic polynomials 1 and X are orthogonal in the required degrees: ∫x dμ=0. The monic polynomial X2−1/3 is orthogonal to 1 because μ2−(1/3)μ0=0, and to X because μ3−(1/3)μ1=0. Uniqueness [F4] identifies p0,p1,p2 as stated. By [F11], 3>0 exists uniquely. The identity X2−1/3=(X−1/3)(X+1/3) gives exactly the two roots ±1/3.

2.2F3F5step 1.2algebra

Here h0=μ0=2, h1=∫x2 dμ=2/3, and both recurrence diagonal entries are zero by the odd moments. Hence β1=h1/h0=1/3, and [F5] gives the displayed J2. Direct multiplication shows its two displayed unit eigenvectors have eigenvalues −1/3 and 1/3; [F6] identifies those as the Gaussian nodes.

3.1F7F8step 1.2step 2.2algebra∎

For either unit eigenvector, the first-coordinate square is 1/2. Applying [F7] with μ0=2 gives each weight as 1. Exactness through degree three is [F8], proving the stated rule.

5 · Examples, counterexamples and false statements

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