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✓ 13 results · all verified · 6 also independently AI-judged
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Gaussian quadrature

1 · Prerequisites

2 · Summary

For a finite positive Borel measure on a compact interval whose support has at least m+1 points, the moment pairing is positive definite through degree m−1 under the exactness-order moment assumptions, and through degree m on the stronger degree-2m branch. The degree-m monic polynomial orthogonal to lower degrees has m simple roots in the interval's interior. Integrating the corresponding Lagrange cardinal polynomials defines the Gaussian weights; polynomial division and orthogonality give exactness through degree 2m−1, and applying exactness to squared cardinal polynomials gives strict positivity.

The moment interface separates two claims. Moments through degree 2m−1 suffice for the node construction and exactness theorem. The Hermite mean-value remainder also needs the degree-2m moment that makes ∫pm2 dμ finite and positive. The proof constructs the degree-m polynomial from the positive degree-(m−1) moment matrix, so it does not assume a degree-m norm on the exactness-only branch.

The three-term recurrence gives a real symmetric tridiagonal Jacobi matrix. Consecutive orthogonal polynomials, equivalently consecutive leading principal Jacobi truncations, have strictly interlacing roots. Its eigenvalues are exactly the Gaussian nodes, and a normalized eigenvector's first-coordinate square times the total mass is the associated weight. The Golub–Welsch procedure uses a finite eigensolver only to propose Ritz data; outward interval residual, Gram, and spectral checks must certify every returned enclosure. A failed finite check returns inconclusive and makes no error claim. Tensor products preserve coordinatewise exactness, with ∏jmj nodes for coordinate orders mj. A separate Tonelli–Fubini argument identifies the algebraic product-moment functional with integration under the finite product measure, so polynomial-chaos coefficient exactness uses a proved measure interface.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Positive-measure Gaussian quadrature moment data

Definition

Let a<b be real numbers, let m≥1 be an integer, and let r∈{2m−1,2m}. A Gaussian quadrature moment datum of order r is a finite positive measure μ on the relative Borel sigma-algebra B([a,b]) (The Borel sigma-algebra of a topological space, Measure spaces, Measures on sigma-algebras, Finite, sigma-finite, and semifinite measures) such that μ([a,b])>0, and whose relative support Sμ:={x∈[a,b]:μ(U)>0 for every relatively open U⊆[a,b] with x∈U} contains at least m+1 distinct points. For every integer 0≤k≤r, require x↦∣x∣k to be integrable with respect to μ (Integrable real and complex functions, and their integrals) and put Mk:=∫[a,b]xk dμ(x). Powers use the natural-exponent recursion of Integer powers am, with x0=1 for every real x, including x=0; thus M0 is the integral of the constant function 1.

The branch r=2m−1 supplies all moments used to construct the monic degree-m orthogonal polynomial and prove rule exactness. The stronger branch r=2m also defines its squared norm and is used for the remainder formula. Because [a,b] is bounded and μ is finite, these absolute-moment integrability conditions follow from finiteness; they are stated to make the two moment interfaces explicit.

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Coordinatewise product-moment functional for tensor polynomials

Definition

For d≥1, let (aj,bj,mj,2mj−1,μj) be Gaussian moment data for each coordinate j=1,…,d, and write Mk(j):=∫xk dμj(x). A coordinatewise tensor polynomial is a finite family (cα), with cα∈R for every index, indexed by multi-indices α=(α1,…,αd) with 0≤αj≤2mj−1, evaluated by P(x1,…,xd)=∑αcα∏j=1dxjαj. Define its coordinatewise product-moment value by I⊗(P):=∑αcα∏j=1dMαj(j). In both sums, list the finite multi-index box in increasing lexicographic order (coordinate 1 first, then coordinate 2, and so on), and take the ordinary finite-list sum in that order. The products use increasing coordinate order j=1,…,d. The real coefficient family is part of the formal polynomial data, so these formulas specify finite real sums. This definition specifies the product-moment functional used for coordinatewise exactness without requiring a measure-product construction.

DefinitionDefinition: AI-adaptedProof: Not applicableOpen item page →

The polynomial moment pairing

Definition

Fix moment data (a,b,m,r,μ) as in Positive-measure Gaussian quadrature moment data. For an integer k with 0≤k≤m and 2k≤r, let Vk=Rk+1 with its coordinatewise real vector-space operations. For c=(c0,…,ck)∈Vk, let Pc(x)=∑j=0kcjxj. This is the evaluation of the zero polynomial when c=0, and otherwise of the formal polynomial obtained by deleting trailing zero coefficients from c (Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials). Define the polynomial moment pairing on Vk by ⟨c,d⟩μ:=∫[a,b]Pc(x)Pd(x) dμ(x). This is a candidate real inner product in the sense of Real and complex inner product spaces, with the inner product linear in the first argument; its well-definedness and positive definiteness under the stated support and moment hypotheses are proved in The polynomial moment pairing is a real inner product ↗.

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passOpen item page →

The polynomial moment pairing is a real inner product

Statement

Let (a,b,m,r,μ) be moment data as in Positive-measure Gaussian quadrature moment data. For 0≤k≤m with 2k≤r, the pairing The polynomial moment pairing is a well-defined real inner product on Rk+1.

Facts & Assumptions

Given: Moment data (a,b,m,r,μ), an integer k with 0≤k≤m and 2k≤r, and coefficient vectors c,d∈Rk+1.

[F1]

Positive-measure Gaussian quadrature moment data: μ is a finite positive Borel measure; its relative support contains at least m+1 points; and ∣x∣j is integrable for every j≤r.

[F2]

The polynomial moment pairing: Pc(x)=∑i=0kcixi and ⟨c,d⟩μ=∫PcPd dμ.

[F3]

A nonzero real polynomial of degree n has no more than n distinct real roots: a nonzero real polynomial of degree at most k has at most k distinct real roots.

[F4]

Monotonicity and nonnegative homogeneity of the nonnegative integral, The nonnegative integral agrees with the simple integral on simple functions, and The integral of a nonnegative simple function: the nonnegative integral is monotone and positively homogeneous, and ∫1U dμ=μ(U) for a Borel set U.

[F5]

The Lebesgue integral is linear on L1(μ): finite sums and real scalar multiples of integrable functions are integrable and their integrals obey the corresponding linearity rules.

[F7]

The reals form a field: real addition and multiplication are distributive and multiplication is commutative.

[F8]

Real and complex inner product spaces, with the inner product linear in the first argument: a real inner product is bilinear, symmetric, nonnegative on the diagonal, and definite.

Proof

1.1F1F2F5algebra

Each monomial xj with j≤2k is integrable by [F1]. Expanding the finite coefficient sums in [F2] expresses PcPd as a finite real linear combination of such monomials, so [F5] makes the product integrable. Distributivity and [F5] then give bilinearity in each coefficient vector.

1.2F2F7algebra

Real multiplication is commutative by [F7], so PcPd=PdPc pointwise and the pairing is symmetric.

1.3F1F2F3choose

If c≠0, its coefficient list defines a nonzero formal polynomial Pc of degree at most k. By [F3], its zero set has at most k points. Since the support in [F1] has at least m+1≥k+1 distinct points, choose a support point s with Pc(s)≠0.

2.1F1F2F4F6step 1.3choosealgebra

Put α=Pc(s)2>0. Continuity in [F6] gives a relative open neighborhood U of s on which Pc(x)2>α/2. This set is Borel and has μ(U)>0 by the support definition. Monotonicity, homogeneity and the indicator identity in [F4] imply ⟨c,c⟩μ=∫Pc2 dμ≥α2μ(U)>0.

3.1step 1.1step 1.2step 2.1F8∎

The pairing is well-defined by step 1.1, real-bilinear and symmetric by steps 1.1–1.2, nonnegative on the diagonal, and zero on the diagonal only for c=0 by step 2.1. These are the real inner product axioms in [F8].

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passOpen item page →

Monic orthogonal polynomials exist uniquely through the quadrature order

Statement

Let (a,b,m,r,μ) be moment data as in Positive-measure Gaussian quadrature moment data, with m≥1 and r≥2m−1. For each j=0,…,m there is a unique monic real polynomial pj of degree j such that ∫[a,b]pj(x)q(x) dμ(x)=0 for every real polynomial q of degree less than j. For j=0, this means p0=1 and the orthogonality condition is vacuous. The j=m polynomial uses moments only through degree 2m−1; its squared norm is asserted only when the moment datum has order 2m.

Facts & Assumptions

Given: Moment data (a,b,m,r,μ) with r≥2m−1.

[F1]

Positive-measure Gaussian quadrature moment data: μ is finite and positive, its relative support has at least m+1 points, and every absolute moment through order 2m−1 is finite.

[F2]

The polynomial moment pairing is a real inner product: for k=m−1, the coefficient-vector pairing ⟨c,d⟩μ=∫PcPd dμ is a real inner product.

[F3]

Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans: Gram–Schmidt applied to a finite independent list in an inner product space gives an orthonormal list with the same successive spans.

[F5]

Rank-nullity: dim⁡FV=nullity⁡T+rank⁡T: for a linear map from a finite-dimensional vector space to itself, trivial kernel implies full-dimensional image and hence surjectivity.

[F6]

The Lebesgue integral is linear on L1(μ): finite polynomial expansions may be integrated term by term whenever the displayed moments are finite.

[F7]

Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials and A finite coefficient vector has a canonical polynomial representative: a coefficient vector represents a formal polynomial; a nonzero highest coefficient determines its degree and leading coefficient.

[F8]

The reals form a field: every nonzero real coefficient has a multiplicative inverse.

Proof

1.1F2F3F4F7F8construct

By [F2], the pairing on the coefficient space Rm for degrees at most m−1 is an inner product. The list e0,…,em−1 is independent by [F4], so [F3] gives orthonormal coefficient vectors u0,…,um−1 whose first j+1 vectors span the same space as e0,…,ej. The polynomial Puj therefore has degree exactly j: it lies in the span of 1,x,…,xj but not in the span through degree j−1. Dividing by its nonzero leading coefficient, which is invertible by [F8], gives a monic pj, orthogonal to every polynomial of degree less than j.

1.2F1F2F4F5algebra

Put Gik=∫xi+k dμ for 0≤i,k<m, and bi=∫xi+m dμ. All entries are finite by [F1]. The matrix G represents the inner product of [F2] in the standard basis. If Gc=0, then cTGc=⟨c,c⟩μ=0, so [F2] gives c=0. Thus the linear map G:Rm→Rm has trivial kernel; [F5] makes it surjective. There is therefore a unique coefficient vector c solving Gc=−b.

2.1F1F2F6F7step 1.2algebraconstruct

Define pm(X)=Xm+∑k=0m−1ckXk. Its leading coefficient is 1, and the ith equation of Gc=−b gives ∫pm(x)xi dμ(x)=0 for each i<m. Linearity [F6] extends this equality to every polynomial of degree less than m. If another monic polynomial had the same orthogonality, its difference from pm would have degree at most m−1 and pair to zero with itself; positive definiteness [F2] makes that difference zero.

3.1step 1.1step 1.2step 2.1∎

Step 1.1 gives the unique monic polynomials below degree m, and steps 1.2–2.1 give the unique degree-m polynomial using only moments through 2m−1. This proves the assertion for every j=0,…,m.

LemmaStatement: Literature-sourcedProof: AI-adaptedprecheck passOpen item page →

Monic orthogonal polynomials satisfy a three-term recurrence

Statement

Let (a,b,m,r,μ) be moment data from Positive-measure Gaussian quadrature moment data with r≥2m−1, and let p0,…,pm be the monic orthogonal polynomials of Monic orthogonal polynomials exist uniquely through the quadrature order. For 0≤j<m put hj:=∫[a,b]pj(x)2 dμ(x),αj:=∫[a,b]x pj(x)2 dμ(x)hj. For 1≤j<m put βj:=hj/hj−1. These numbers are defined, hj>0, and βj>0. The recurrence is xp0=p1+α0p0 and, for 1≤j<m, xpj=pj+1+αjpj+βjpj−1. Only moments through degree 2m−1 are used.

Facts & Assumptions

Given: The stated moment data and monic orthogonal family.

[F1]

Positive-measure Gaussian quadrature moment data: moments through degree 2m−1 are finite.

[F2]

The polynomial moment pairing is a real inner product: the moment pairing is symmetric, bilinear, and positive definite on each such space.

[F3]

Monic orthogonal polynomials exist uniquely through the quadrature order: each pj is monic of degree j and orthogonal to every polynomial of smaller degree.

[F4]

Degree inequalities for sums and products over a commutative ring: a sum with cancellation of top coefficients has degree bounded by the largest remaining degree; the leading coefficient of a product is the product of leading coefficients.

[F5]

The Lebesgue integral is linear on L1(μ) and The reals form a field: the displayed polynomial integrals are linear and real division by a nonzero norm is defined.

Proof

1.1F1F2F3F5algebra

For j<m, pj2 and xpj2 have degrees at most 2j and 2j+1, both at most 2m−1. Thus the moments in hj and αj are finite by [F1]. Since pj is nonzero, [F2] gives hj>0, so αj is defined; for j≥1, the same positivity for pj−1 gives βj>0.

1.2F3F4construct

The polynomials p0,…,pj form a basis for the polynomials of degree at most j: their distinct degrees and unit leading coefficients permit successive subtraction of the top term. Because xpj−pj+1 has degree at most j by cancellation of the leading term, write it in this basis.

2.1F2F3step 1.2algebra

For k≤j−2, symmetry of the real pairing gives ⟨xpj,pk⟩μ=⟨pj,xpk⟩μ=0, since xpk has degree at most j−1. As the basis in step 1.2 is orthogonal and each squared norm is positive by [F2], the expansion has no pk term for k≤j−2. Its pj coefficient is ⟨xpj,pj⟩μ/hj=αj.

2.2F2F3step 1.1algebra

For j≥1, the pj−1 coefficient is ⟨xpj,pj−1⟩μhj−1=⟨pj,xpj−1⟩μhj−1=hjhj−1=βj. Indeed, xpj−1−pj has degree at most j−1, so it is orthogonal to pj by [F3].

3.1step 1.2step 2.1step 2.2algebra∎

The expansion in step 1.2 therefore has only the displayed pj and, when j≥1, pj−1 terms. Rearranging it yields the stated recurrence for every 0≤j<m.

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

The Gauss orthogonal polynomial has simple roots inside the interval

Statement

Let (a,b,m,r,μ) be moment data from Positive-measure Gaussian quadrature moment data, with r≥2m−1, and let pm be the monic orthogonal polynomial from Monic orthogonal polynomials exist uniquely through the quadrature order. Then pm has exactly m distinct roots a<x1<⋯<xm<b, each simple, and pm(X)=∏i=1m(X−xi).

Facts & Assumptions

Given: The stated moment data and its degree-m monic orthogonal polynomial pm.

[F1]

Positive-measure Gaussian quadrature moment data: the relative support of μ contains at least m+1 distinct points and all moments through degree 2m−1 are finite.

[F2]

Monic orthogonal polynomials exist uniquely through the quadrature order: pm is monic of degree m and ∫pmq dμ=0 for every polynomial q of degree less than m.

[F3]

A nonzero real polynomial of degree n has no more than n distinct real roots: a nonzero degree-d polynomial has at most d distinct real roots.

[F4]

Monotonicity and nonnegative homogeneity of the nonnegative integral, The nonnegative integral agrees with the simple integral on simple functions, and The integral of a nonnegative simple function: if a nonnegative integrable function is bounded below by c>0 on a Borel set U, its integral is at least cμ(U).

[F5]

The Lebesgue integral is linear on L1(μ): the integral is linear on integrable real functions.

[F6]

A finite product of monic linear factors is monic and Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials: the product of the linear factors at m distinct real numbers is monic of degree m.

[F8]

The reals form a totally ordered field: the real order is total, so an interval in R that avoids 0 lies wholly on one side of 0.

[F9]

Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials: formal polynomials are their coefficient lists, so cancellation of equal top coefficients leaves degree at most one less.

Proof

1.1F2F3F6F7F8F10assume-contraalgebraconstruct

Call an interior root sign-changing if pm has opposite nonzero signs on the two adjacent intervals containing no root. Let R be the set of such roots; it is finite by [F3]. Suppose, for contradiction, that R has k<m elements, and put q(X)=∏s∈R(X−s), with the empty product equal to 1. Between consecutive roots of pm, the product h=pmq is continuous and nonzero, so its image is an interval avoiding 0 and its sign is constant by [F7]–[F8]. Crossing a root in R flips both factors; crossing any other root of pm flips neither product sign. Thus h has one weak sign on [a,b].

2.1F2step 1.1

Since deg⁡q=k<m, orthogonality [F2] gives ∫h dμ=0.

2.2F1F3F6F10step 1.1choose

The polynomial pm has at most m distinct roots by [F3]. The support condition [F1] therefore supplies a support point y with pm(y)≠0; as every root of q is a root of pm, also h(y)≠0. Continuity of h gives a relative open neighborhood U of y on which ∣h∣≥c for some c>0 and the sign of h is unchanged. This neighborhood is Borel and μ(U)>0 by the definition of support.

3.1F4F5step 2.1step 2.2algebra

If h≥0 on [a,b], [F4] gives ∫h dμ≥cμ(U)>0. If h≤0, apply [F4] to −h and use [F5] to obtain ∫h dμ<0. Both contradict step 2.1, so k≥m.

4.1F2F3F6F9step 3.1discharge-contradictionalgebra∎

By [F3], pm has at most m distinct roots, and step 3.1 gives at least m, so k=m. Write them as x1,…,xm∈(a,b) and let s(X)=∏i=1m(X−xi). Both s and pm are monic of degree m by [F2, F6]. Their degree-m coefficients cancel, so their difference is zero or has degree at most m−1 by [F9]. It vanishes at all m distinct xi; if nonzero, [F3] is contradicted. Thus pm=s, so each root occurs as exactly one linear factor and is simple.

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

Double-node Hermite interpolation has the stated pointwise remainder

Statement

Let m≥1, let a<b, let x1<⋯<xm lie in (a,b), and let f∈C2m([a,b],R). Let ℓi be the Lagrange cardinal polynomials for these nodes as in Cardinal-polynomial interpolation. Define H(t):=∑i=1m[f(xi)(1−2ℓi′(xi)(t−xi))+f′(xi)(t−xi)]ℓi(t)2. Then H has degree at most 2m−1, and H(xi)=f(xi) and H′(xi)=f′(xi) for every i. If x∈[a,b] is not a node, there is ξx∈(a,b) such that f(x)−H(x)=f(2m)(ξx)(2m)!R∏i=1m(x−xi)2, where (2m)!R:=∏j=12mj. At a node the interpolation error and the displayed node product are both zero.

Facts & Assumptions

Given: Distinct interior nodes x1<⋯<xm, f∈C2m([a,b]), and a query x∈[a,b].

[F1]

Cardinal-polynomial interpolation and Cardinal interpolation functions are polynomials of the stated degrees: ℓi is a polynomial of degree at most m−1, ℓi(xj)=1 when i=j and 0 otherwise, and the node product ω(t)=∏i(t−xi) is nonzero off the nodes.

[F2]

Higher derivatives and the classes Ck and C∞: all derivatives of f through order 2m exist on [a,b] and are continuous there.

[F5]

Higher-order Rolle theorem: a function with n+1 distinct ordered zeros, continuous on their closed span and n-times differentiable in its interior, has an nth-derivative zero between the extreme zeros.

[F6]

A finite product of monic linear factors is monic: the product of 2m monic linear factors is monic of degree 2m.

[F7]

The highest derivative of a polynomial: a polynomial of degree at most 2m has constant 2mth derivative equal to (2m)!R times its coefficient of X2m.

[F8]

Degree inequalities for sums and products over a commutative ring and The reals form a field: sums and products of the displayed polynomial factors have the stated degree bounds and coefficient arithmetic.

Proof

1.1F1F3algebra

For each i, set hi(t)=(1−2ℓi′(xi)(t−xi))ℓi(t)2,ki(t)=(t−xi)ℓi(t)2. At xi, ℓi(xi)=1, so hi(xi)=1, hi′(xi)=0, ki(xi)=0, and ki′(xi)=1 by the product rule [F3]. At xj with j≠i, ℓi(xj)=0, so both ℓi2 and its derivative vanish there; hence hi(xj)=hi′(xj)=ki(xj)=ki′(xj)=0. Therefore the displayed sum defining H satisfies every value and first-derivative condition.

1.2F1F8construct

Each ℓi has degree at most m−1 by [F1]. Thus ℓi2 has degree at most 2m−2, and multiplication by an affine polynomial gives degree at most 2m−1 by [F8]. The finite sum H also has degree at most 2m−1.

2.1F1F3step 1.1constructalgebra

Suppose x is not a node. By [F1], ω(x)2≠0, so put c=(f(x)−H(x))/ω(x)2 and define g(t)=f(t)−H(t)−cω(t)2. At every node, step 1.1 and ω(xi)=0 give g(xi)=0 and g′(xi)=0, since (ω2)′=2ωω′ by [F3]; by the choice of c, also g(x)=0.

3.1F2F3F4step 2.1choose

Sort the m nodes and x as z0<⋯<zm. Rolle's theorem on each consecutive interval gives a zero of g′ strictly between its endpoints; these m zeros are distinct and are not nodes. Along with g′(xi)=0 at the m nodes from step 2.1, this gives 2m distinct ordered zeros of g′.

4.1F2F3F4F5step 3.1

The function g′ is continuous on the closed span of those zeros and is (2m−1)-times differentiable on its interior by [F2]–[F4]. Apply [F5] to those 2m zeros. There is ξx∈(a,b) with g(2m)(ξx)=0.

5.1F6F7step 1.2step 2.1step 4.1algebra

By step 1.2, H(2m)=0. By [F6], ω2 is monic of degree 2m, so [F7] gives (ω2)(2m)=(2m)!R. Differentiating the definition of g in step 2.1 at ξx therefore gives 0=f(2m)(ξx)−c(2m)!R.

6.1F1F9step 1.1step 2.1step 5.1algebra∎

By [F9], (2m)!R≠0, so step 5.1 gives c=f(2m)(ξx)/(2m)!R. Substituting this into the definition of c in step 2.1 proves the remainder formula. At a node, step 1.1 gives zero interpolation error and ω=0.

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

The m-node Gaussian quadrature rule for a positive measure

Definition

Let (a,b,m,2m−1,μ) be moment data as in Positive-measure Gaussian quadrature moment data. Let pm be its monic degree-m orthogonal polynomial and write its ordered roots as a<x1<⋯<xm<b, as established in The Gauss orthogonal polynomial has simple roots inside the interval. For i=1,…,m, define the Lagrange cardinal polynomial ℓi(X):=∏1≤j≤mj≠iX−xjxi−xj using the convention of Cardinal-polynomial interpolation. Define the Gaussian quadrature weights and the m-node Gaussian rule by wi:=∫[a,b]ℓi(x) dμ(x),Qm(f):=∑i=1mwif(xi) for any real-valued function f defined at the nodes. This definition asserts no accuracy; well-definedness of the nodes, cardinal polynomials, weights, and finite sum is proved in The Gaussian nodes, cardinal polynomials and weights are well-defined ↗.

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The Jacobi matrix spectrum is the set of Gauss nodes

Statement

Let (a,b,m,r,μ) be moment data from Positive-measure Gaussian quadrature moment data with r≥2m−1. For 0≤j<m, let hj,αj be as in Monic orthogonal polynomials satisfy a three-term recurrence, and for 1≤j<m let βj=hj/hj−1. Put β0=0 and define the real symmetric tridiagonal Jacobi matrix Jm=(α0β1β1α1⋱⋱⋱βm−1βm−1αm−1), with the evident one-by-one interpretation for m=1. If a<x1<⋯<xm<b are the roots of pm, then spec⁡(Jm)={x1,…,xm}, where spectrum denotes the set of eigenvalues as in Eigenvalues, eigenvectors, eigenspaces Eλ(T)=ker⁡(T−λI), and the spectrum σF(T) of an endomorphism; every eigenvalue is simple. If qj:=pj/hj for 0≤j<m, then (q0(xi),…,qm−1(xi))T is a nonzero eigenvector of Jm for xi.

Facts & Assumptions

Given: Moment data and the monic orthogonal polynomials through degree m.

[F1]

Monic orthogonal polynomials satisfy a three-term recurrence: xpj=pj+1+αjpj+βjpj−1 for 1≤j<m, and xp0=p1+α0p0.

[F2]

The Gauss orthogonal polynomial has simple roots inside the interval: the degree-m polynomial pm has exactly m distinct roots xi∈(a,b).

[F3]

Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis.

[F5]

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

Proof

1.1F1F2F5algebra

By [F1] and [F5], the normalized polynomials qj:=pj/hj satisfy xqj=βj+1qj+1+αjqj+βjqj−1 for 0≤j<m−1, with q−1=0. For the final index, the monic recurrence gives xpm−1=pm+αm−1pm−1+βm−1pm−2; at a root xi the pm term vanishes. After dividing by hm−1, this is the final row of Jmzi=xizi for zi=(q0(xi),…,qm−1(xi))T. When m=1, the identity xp0=p1+α0p0 gives the same conclusion at the sole root.

2.1F2step 1.1F5algebra

The vector zi is nonzero because q0=1/h0 and h0=μ([a,b])>0. Thus each of the m distinct roots in [F2] is an eigenvalue of Jm.

3.1F2F3F4F6step 2.1∎

The matrix Jm is real symmetric by its definition, so [F4] makes its endomorphism self-adjoint on Rm. By [F3], it has an orthonormal eigenbasis of m vectors; hence it has exactly m eigenvalues counted with multiplicity. The m distinct eigenvalues already exhibited in step 2.1 exhaust this list, so each is simple and there are no others. By [F6], this eigenvalue set is spec⁡(Jm).

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Consecutive Gaussian orthogonal polynomials and Jacobi truncations strictly interlace

Statement

Let (a,b,m,r,μ) be Gaussian moment data with r≥2m−1 and monic orthogonal polynomials p0,…,pm. For every 1≤j<m, write the roots of pj as x1<⋯<xj and those of pj+1 as y1<⋯<yj+1. Then a<y1<x1<y2<x2<⋯<yj<xj<yj+1<b. Equivalently, the spectra of consecutive leading principal Jacobi truncations Jj,Jj+1 strictly interlace. If m=1, there is no pair of positive consecutive degrees and the assertion is vacuous.

Facts & Assumptions

Given: The moment data and its monic orthogonal family.

[F1]

Positive-measure Gaussian quadrature moment data and Monic orthogonal polynomials exist uniquely through the quadrature order permit the same measure at each smaller quadrature order k≤m, retaining moments through 2k−1; uniqueness identifies each shorter orthogonal family with the prefix p0,…,pk.

[F2]

The Gauss orthogonal polynomial has simple roots inside the interval gives k simple roots in (a,b) and the monic factorization of pk for each 1≤k≤m.

[F3]

Monic orthogonal polynomials satisfy a three-term recurrence gives pj+1(X)=(X−αj)pj(X)−βjpj−1(X) with βj>0 for 1≤j<m, and p0=1.

[F5]

The reals form a totally ordered field gives the sign rules for the finite products in the monic factorization.

[F6]

The Jacobi matrix spectrum is the set of Gauss nodes identifies the spectrum of Jk with the roots of pk; its coefficients are the same prefix recurrence coefficients.

Proof

1.1givenF1F2F3F4F5algebra

The reduced-order uses in [F1] are valid: the original support has at least m+1≥k+1 points and the absolute moments through 2k−1 are among those assumed. By [F2], every pk has the stated simple roots and monic factorization. If m=1, there is no pair to prove. For the remaining case m≥2, take j=1; if x1 is the root of p1, [F3] gives p2(x1)=−β1<0. At both a and b, the degree-two monic factorization [F2] gives p2(a)>0 and p2(b)>0. By [F4] there is one root on each side of x1 inside (a,b), proving the first pair when m≥2.

1.2F2F3F4F5givenalgebra

For 2≤j<m, assume the roots of pj−1 strictly interlace those x1<⋯<xj of pj. At xi, exactly j−i roots of the monic polynomial pj−1 lie to its right and none equals it. Its factorization [F2] therefore makes its sign (−1)j−i. By [F3], the sign of pj+1(xi)=−βjpj−1(xi) is (−1)j−i+1. These signs alternate, so [F4] supplies a root in each (xi,xi+1). Also [F2] gives sign (−1)j+1 for pj+1(a) and positive sign for pj+1(b); these are opposite to the signs (−1)j at x1 and −1 at xj. Thus [F4] supplies an additional root in each of (a,x1) and (xj,b).

2.1F1F2F6step 1.1step 1.2inductionalgebra∎

These j+1 disjoint intervals contain j+1 distinct roots, and [F2] says there are exactly that many. They therefore exhaust the roots of pj+1 in the required order. Starting with step 1.1, finite induction through step 1.2 proves every pair through pm. By [F6], the same ordered roots are the eigenvalues of the leading principal truncations, since their recurrence coefficients are identical prefixes. This proves the spectral assertion as well; when m=1 the quantified pair set is empty.

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The Gaussian nodes, cardinal polynomials and weights are well-defined

Statement

For moment data with order 2m−1, the nodes and rule defined in The m-node Gaussian quadrature rule for a positive measure exist as stated: the roots can be ordered in (a,b), every cardinal polynomial is defined, every weight integral is finite, and Qm(f) is a finite real number whenever f is real-valued at the nodes.

Facts & Assumptions

Given: Moment data (a,b,m,2m−1,μ) and the rule of The m-node Gaussian quadrature rule for a positive measure.

[F1]

Positive-measure Gaussian quadrature moment data: moments through degree 2m−1 are finite.

[F3]

Cardinal-polynomial interpolation: for distinct nodes, ℓi(X)=∏j≠i(X−xj)/(xi−xj); every denominator is a nonzero node difference.

[F4]

Cardinal interpolation functions are polynomials of the stated degrees: each ℓi is a polynomial of degree at most m−1 and has the cardinal values at the nodes.

[F5]

The Lebesgue integral is linear on L1(μ): a finite linear combination of integrable monomials is integrable and its integral is the corresponding finite linear combination of moments.

Proof

1.1F2F3F4construct

By [F2], the roots of pm form a finite set of exactly m distinct points in (a,b), so sorting them defines x1<⋯<xm without ambiguity. Their pairwise differences are nonzero, so [F3] defines every ℓi. By [F4], each cardinal polynomial has degree at most m−1.

1.2F1F4F5

Each ℓi is a finite linear combination of monomials of degrees at most m−1. These moments are finite by [F1], so [F5] makes every wi=∫ℓi dμ finite.

2.1F1step 1.2algebra∎

For any real values f(x1),…,f(xm), the expression ∑iwif(xi) has finitely many finite real summands, hence is a defined real number. This establishes the stated well-definedness of Qm(f).

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Tensor-product Gaussian quadrature

Definition

Let d≥1 and, for each coordinate j=1,…,d, let Qj(f)=∑i=1mjwi(j)f(xi(j)) be a well-defined Gaussian rule as in The m-node Gaussian quadrature rule for a positive measure and The Gaussian nodes, cardinal polynomials and weights are well-defined. Its tensor-product quadrature rule is Q⊗(f):=∑i1=1m1⋯∑id=1md(∏j=1dwij(j))f(xi1(1),…,xid(d)). Its node set is the Cartesian product of the one-dimensional node sets; the displayed finite sum is its value on any real-valued function defined on that node set.

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Gaussian quadrature is exact through degree 2m−1 and has positive weights

Statement

Let (a,b,m,2m−1,μ) be moment data as in Positive-measure Gaussian quadrature moment data, and let Qm and wi be the rule and weights of The m-node Gaussian quadrature rule for a positive measure. For every real polynomial f of degree at most 2m−1, ∫[a,b]f(x) dμ(x)=Qm(f)=∑i=1mwif(xi). Moreover, wi>0 for every i and ∑iwi=μ([a,b]).

Facts & Assumptions

Given: Moment data, its m-node Gaussian rule, and a real polynomial f of degree at most 2m−1.

[F1]

Positive-measure Gaussian quadrature moment data: all moments through degree 2m−1 are finite.

[F2]

Monic orthogonal polynomials exist uniquely through the quadrature order: pm is monic of degree m and is orthogonal to all polynomials of degree less than m.

[F3]

The Gauss orthogonal polynomial has simple roots inside the interval and The Gaussian nodes, cardinal polynomials and weights are well-defined: the nodes are the distinct roots of pm, and the rule's weights and cardinal polynomials are defined.

[F4]

Division by a monic polynomial over a commutative ring: f=pms+t with either t=0 or deg⁡t<m.

[F5]

The m-node Gaussian quadrature rule for a positive measure: wi=∫ℓi dμ and Qm(f)=∑iwif(xi).

[F6]

Cardinal-polynomial interpolation, Cardinal interpolation functions are polynomials of the stated degrees, and Existence and uniqueness of the cardinal-polynomial interpolant: every polynomial t of degree at most m−1 satisfies t(x)=∑it(xi)ℓi(x).

[F7]

The polynomial moment pairing is a real inner product: a nonzero polynomial of degree at most m−1 has strictly positive squared integral.

[F8]

The Lebesgue integral is linear on L1(μ): the integral distributes over the finite polynomial sums used below.

Proof

1.1givenF1F2F4F8algebra

By [F4], write f=pms+t, where t=0 or deg⁡t<m. If s≠0, the leading term of pms has degree m+deg⁡s and cannot cancel against t, so m+deg⁡s=deg⁡f≤2m−1 and deg⁡s<m. The moments in these polynomials are finite by [F1]. If s=0, the integral of pms=0 is zero; otherwise orthogonality [F2] gives ∫pms dμ=0. Thus ∫f dμ=∫t dμ by [F8].

1.2F2F3F4F5algebra

At every node xi, pm(xi)=0, so f(xi)=t(xi) and [F5] gives Qm(f)=Qm(t).

1.3F5F6F8algebra

The cardinal interpolation identity [F6] gives t(x)=∑it(xi)ℓi(x). Integrating this finite sum and using [F5] and [F8] yields ∫t dμ=∑it(xi)∫ℓi dμ=∑iwit(xi)=Qm(t).

2.1step 1.1step 1.2step 1.3

Steps 1.1–1.3 give ∫f dμ=∫t dμ=Qm(t)=Qm(f), proving exactness for every such f.

3.1F6F7step 2.1algebra

For a fixed i, ℓi2 has degree at most 2m−2, so step 2.1 applied to this polynomial gives ∫ℓi2 dμ=Qm(ℓi2)=∑jwjℓi(xj)2=wi by the cardinal values in [F6]. The cardinal polynomial is nonzero because ℓi(xi)=1; [F7] therefore gives wi>0.

4.1givenF9step 2.1∎

Applying exactness to the constant polynomial 1 and then [F9] gives ∑iwi=∫1 dμ=μ([a,b]).

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Gaussian quadrature has the Hermite mean-value remainder

Statement

Let (a,b,m,2m,μ) be moment data as in Positive-measure Gaussian quadrature moment data, let Qm be its Gaussian rule, and let f∈C2m([a,b],R). Then there is ξ∈(a,b) such that ∫[a,b]f(x) dμ(x)−Qm(f)=f(2m)(ξ)(2m)!R∫[a,b]pm(x)2 dμ(x), where pm(X)=∏i=1m(X−xi) is the monic node polynomial and (2m)!R:=∏j=12mj.

Facts & Assumptions

Given: Moment data of order 2m, its Gaussian rule, and a real function f∈C2m([a,b]).

[F1]

Positive-measure Gaussian quadrature moment data: μ is finite and positive, its support contains at least m+1 points, and moments through degree 2m are finite.

[F2]

The m-node Gaussian quadrature rule for a positive measure and Gaussian quadrature is exact through degree 2m−1 and has positive weights: Qm is the finite Gaussian rule, is exact through degree 2m−1, and its nodes are the roots of pm.

[F3]

Double-node Hermite interpolation has the stated pointwise remainder: for the nodes xi, the explicit polynomial H has degree at most 2m−1, matches f,f′ at the nodes, and for every non-node x the pointwise remainder is f(x)−H(x)=pm(x)2f(2m)(ξx)/(2m)!R for some ξx∈(a,b).

[F4]

The polynomial moment pairing is a real inner product: since r=2m, ∫pm2 dμ is finite and strictly positive.

[F7]

The Lebesgue integral is linear on L1(μ): integrable functions may be added and integrated linearly.

[F9]

Continuity of f:A→R at a point of A and on A: the ε-δ condition, its agreement with lim⁡x→cf(x)=f(c) at a limit point, and continuity at an isolated point and The Borel sigma-algebra of a topological space: a continuous function on [a,b] is Borel measurable, since preimages of relative open sets are relative open and those sets generate B([a,b]).

Proof

1.1F1F3F4F5F6F9F10algebra

The function f and the polynomial H from [F3] are continuous, hence Borel measurable by [F9]–[F10]. They are bounded on the compact interval by [F5]. Since μ([a,b])<∞ by [F1], monotonicity and homogeneity in [F6] give ∫∣f∣ dμ<∞ and ∫∣H∣ dμ<∞. Also pm2 is integrable by [F4]. Thus all integrals below are finite.

1.2F3F5F8algebra

Let A=min⁡[a,b]f(2m) and B=max⁡[a,b]f(2m), which exist by [F5]. The pointwise remainder [F3], and the zero error at nodes, imply for every x∈[a,b] that A(2m)!Rpm(x)2≤f(x)−H(x)≤B(2m)!Rpm(x)2. The multiplier pm(x)2 is nonnegative.

2.1F2F3step 1.1algebra

By [F3], H(xi)=f(xi) and deg⁡H≤2m−1. Exactness [F2] gives ∫H dμ=Qm(H)=Qm(f), so the quadrature error is ∫(f−H) dμ.

2.2F4F6F7F8step 1.2algebra

Integrating the nonnegative differences in step 1.2 and using [F6]–[F7] gives A(2m)!R∫pm2 dμ≤∫(f−H) dμ≤B(2m)!R∫pm2 dμ. By [F4], hm:=∫pm2 dμ>0. Define γ:=(2m)!R∫(f−H) dμ/hm; the displayed bounds give A≤γ≤B.

3.1F5step 2.2caseschoose

If A=B, f(2m) is constant and any point of (a,b) has value γ. If A<γ<B, choose points attaining A and B by [F5]. They are distinct; the intermediate value property on the segment between them gives a point strictly between the extrema where f(2m)=γ, and that point lies in (a,b).

3.2F1F3F6F8step 2.2casesalgebra

Suppose γ=A<B and f(2m) has no interior point with value A. Then f(2m)(t)>A for every t∈(a,b). For every non-node x, [F3] uses some ξx∈(a,b), and so d(x):=f(x)−H(x)−Apm(x)2/(2m)!R>0 because pm(x)≠0. At nodes, d=0. There is a support point y not among the m nodes by [F1]; continuity makes d≥c>0 on a relative open neighborhood U of y, and μ(U)>0. Thus [F6] gives ∫d dμ>0. But γ=A and step 2.2 imply ∫d dμ=0, a contradiction. Hence some interior point has derivative value γ.

3.3F1F3F6F8step 2.2casesalgebra

The case γ=B>A is analogous: if f(2m)(t)<B throughout (a,b), the nonnegative continuous function e(x):=Bpm(x)2/(2m)!R−(f(x)−H(x)) is strictly positive at every non-node, hence on a positive-measure neighborhood of a support point outside the nodes. This contradicts γ=B in step 2.2. Therefore there is an interior point with derivative value γ.

4.1step 2.1step 2.2step 3.1step 3.2step 3.3algebra∎

In every case there is ξ∈(a,b) with f(2m)(ξ)=γ. Substitute the definition of γ, use step 2.1, and rearrange to obtain the stated remainder formula.

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Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square

Statement

Let (a,b,m,2m−1,μ) be moment data and let Qm have nodes xi and weights wi as in The m-node Gaussian quadrature rule for a positive measure. Let Jm be the Jacobi matrix of The Jacobi matrix spectrum is the set of Gauss nodes, and let vi be any unit eigenvector of Jm for eigenvalue xi. Then wi=μ([a,b]) ∣vi,1∣2.

Facts & Assumptions

Given: Moment data, its Gaussian rule, its Jacobi matrix, and a unit eigenvector vi for node xi.

[F1]

The m-node Gaussian quadrature rule for a positive measure: wi=∫ℓi dμ and the nodes are the roots of pm.

[F2]

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

[F3]

Monic orthogonal polynomials exist uniquely through the quadrature order and Monic orthogonal polynomials satisfy a three-term recurrence: p0=1, the pj are pairwise orthogonal, and their squared norms hj are positive.

[F4]

The Jacobi matrix spectrum is the set of Gauss nodes: the vectors (p0(xi)/h0,…,pm−1(xi)/hm−1)T are nonzero eigenvectors of Jm with eigenvalue xi, the nodes are distinct, and every eigenvalue is simple.

[F6]

Cardinal interpolation functions are polynomials of the stated degrees: ℓi has degree at most m−1 and ℓi(xk)=δik.

[F7]

The polynomial moment pairing and Monic orthogonal polynomials satisfy a three-term recurrence define h0=∫p02 dμ. The integral of a nonnegative simple function and The nonnegative integral agrees with the simple integral on simple functions give ∫1 dμ=μ([a,b]) by the one-cell simple function on the whole interval.

Proof

1.1F3F4construct

Put qj=pj/hj for j=0,…,m−1. By [F3], they are orthonormal. Their degrees are 0,…,m−1 with nonzero leading coefficients, so successive subtraction of top terms shows they span the degree-at-most-m−1 polynomial space and are a basis. For a fixed node xi, set zi=(q0(xi),…,qm−1(xi))T. By [F4], zi is a nonzero eigenvector of Jm for xi.

1.2F4F5algebraconstruct

For i≠k, self-adjointness gives xi⟨zi,zk⟩=⟨Jmzi,zk⟩=⟨zi,Jmzk⟩=xk⟨zi,zk⟩. Since the nodes are distinct by [F4], their difference is nonzero and the pairing is zero. Normalize these vectors to ui=zi/∥zi∥2; their first coordinates satisfy ∣ui,1∣2=q0(xi)2∑j=0m−1qj(xi)2. The simplicity assertion in [F4], together with the orthonormal eigenbasis [F5], makes each eigenspace one-dimensional. Therefore the arbitrary unit eigenvector vi in the Statement is ui or −ui, and its first-coordinate square is the same.

1.3F1F2F6algebra

Expand ℓi in the orthonormal basis q0,…,qm−1. Its coefficient of qj is ∫ℓiqj dμ=Qm(ℓiqj)=wiqj(xi), because deg⁡(ℓiqj)≤2m−2 and ℓi(xk)=δik by [F6].

2.1F2F6step 1.3algebra

By [F6], ℓi2 has degree at most 2m−2 and Qm(ℓi2)=wi, so exactness [F2] gives ∫ℓi2 dμ=wi. Taking squared norms of the expansion in step 1.3 gives wi=∫ℓi2 dμ=wi2∑j=0m−1qj(xi)2. Since wi>0 by [F2], it follows that wi=1/∑jqj(xi)2.

3.1F1F3F7step 1.2step 2.1algebra∎

Since p0=1 by [F3], [F7] gives h0=∫p02 dμ=∫1 dμ=μ([a,b]); thus one has q0(xi)2=1/μ([a,b]). Substituting this and step 2.1 into step 1.2 yields ∣vi,1∣2=wi/μ([a,b]), which is the claimed formula.

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Tensor-product Gaussian quadrature preserves coordinatewise exactness

Statement

For d≥1, take one-dimensional Gaussian rules Qj with orders mj and moment data (aj,bj,mj,2mj−1,μj), and form their tensor rule Q⊗ as in Tensor-product Gaussian quadrature. For every coordinatewise tensor polynomial P of Coordinatewise product-moment functional for tensor polynomials, Q⊗(P)=I⊗(P). The tensor rule has exactly ∏j=1dmj distinct nodes. In particular, using the same m≥2 nodes in each coordinate requires md node evaluations, exponential growth in d.

Facts & Assumptions

Given: The finite family of one-dimensional Gaussian rules and a coordinatewise tensor polynomial.

[F1]

Tensor-product Gaussian quadrature: the tensor rule sums over all node tuples and multiplies the one-dimensional weights.

[F2]

Coordinatewise product-moment functional for tensor polynomials: I⊗ acts on a monomial ∏jxjαj by the product of its one-dimensional moments and extends by finite linearity.

[F3]

Gaussian quadrature is exact through degree 2m−1 and has positive weights: each coordinate rule is exact on every monomial of degree at most 2mj−1.

[F5]

The reals form a field: multiplication and finite sums obey distributivity.

Proof

1.1F1F4F5algebra

For a coordinate monomial Pα(x)=∏j=1dxjαj, substitute the rule definition [F1] and distribute the finite sums: Q⊗(Pα)=∏j=1d(∑ij=1mjwij(j)(xij(j))αj).

2.1F2F3step 1.1

Since 0≤αj≤2mj−1, [F3] makes the jth factor in step 1.1 equal to Mαj(j). Their product is exactly I⊗(Pα) by [F2].

3.1F1F2step 2.1algebra

A coordinatewise tensor polynomial is a finite linear combination of these monomials. Both Q⊗ and I⊗ are linear finite sums, so step 2.1 extends term by term and proves Q⊗(P)=I⊗(P).

4.1F1F4algebra∎

Each one-dimensional node list has mj distinct entries by The Gauss orthogonal polynomial has simple roots inside the interval. Two different tuples differ in at least one coordinate, whose node entries are distinct, so they give different points. The nested index loops in [F1] enumerate m1 tuples for d=1; appending coordinate d+1 replaces each prior tuple by exactly md+1 distinct extensions. Iterating this finite recurrence gives exactly ∏jmj nodes, hence md when every mj=m≥2.

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Tensor product moments equal integration against the finite product measure

Statement

For the Gaussian coordinate data of Coordinatewise product-moment functional for tensor polynomials, let μ=μ1⊗⋯⊗μd on the finite product of the relative Borel interval spaces, with binary products iterated from left to right. Every real coordinatewise tensor polynomial P in that definition is integrable and ∫P dμ=I⊗(P). This supplies a measure interpretation of the algebraic functional without redefining it. The conclusion also applies when each marginal measure is a probability law.

Facts & Assumptions

Given: d≥1, the finite positive coordinate moment measures and the real finite polynomial coefficients.

[F1]

Positive-measure Gaussian quadrature moment data gives finite factor measures and finite absolute moments through each required coordinate degree; Coordinatewise product-moment functional for tensor polynomials fixes the finite coefficient and product/sum order.

[F2]

For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique gives each binary product measure, its rectangle formula and sigma-finiteness.

[F3]

Tonelli's theorem for nonnegative measurable functions on a sigma-finite product factors iterated integrals of nonnegative product-measurable functions.

[F4]

Fubini's theorem for L^1 functions on a sigma-finite product gives iterated integration for integrable functions; The Lebesgue integral is linear on L1(μ) gives scalar and finite-sum linearity.

Proof

1.1givenF1F2F5induction

Each factor measure is finite, hence sigma-finite. Iterating [F2] constructs the finite product and makes it finite, with total mass equal to the product of the finite factor masses. For the product of intervals, the product sigma-algebra is the trace of the Euclidean Borel sigma-algebra: [F5] gives the binary Euclidean equality, and tracing its rectangle generators to the interval factors gives the corresponding trace equality; finite induction gives the d-factor case. Hence each coordinate monomial and its absolute value are product-measurable by [F5].

2.1F1F3F4step 1.1inductionalgebra

Fix an allowed multi-index α. Apply [F3] successively to the nonnegative function ∏j=1d∣xj∣αj. At each step the inner one-variable integral is the finite absolute moment of the last coordinate, and it factors out as a nonnegative finite scalar. Induction therefore gives ∫∏j∣xj∣αj dμ=∏j∫∣xj∣αj dμj<∞. Thus the signed real monomial is integrable.

3.1F1F4step 2.1inductionalgebra

Apply [F4] to that integrable monomial, integrating the last coordinate first. Its one-variable signed moment is Mαd(d); repeat for the preceding coordinates. Each use is legitimate by the absolute integrability in step 2.1, and finite induction yields ∫∏jxjαj dμ=∏jMαj(j). At d=1 this is the moment definition itself.

4.1F1F4step 3.1algebra∎

A finite real linear combination of these integrable monomials is integrable by [F4]. Integrating that combination term by term in the fixed coefficient order from [F1] and substituting step 3.1 gives ∫P dμ=∑αcα∏jMαj(j)=I⊗(P). No probability normalization or new choice assumption was used.

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A residual and spectral gap bound a Gaussian weight error

Statement

Let (a,b,m,2m−1,μ) be moment data. Put μ0:=μ([a,b])>0. Let J be the m×m real symmetric Jacobi matrix of The Jacobi matrix spectrum is the set of Gauss nodes, with distinct eigenvalues λ1<⋯<λm and Gaussian weights wi of Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square. Fix i, a nonzero vector v∈Rm, a real number θ, and ρ≥0. Suppose ∥v∥2≥s>0 and ∥Jv−θv∥2≤ρ,g:=min⁡k≠i∣λk−θ∣>0. For m≥2, ∣wi−μ0v12∥v∥22∣≤22 μ0ρsg.

Facts & Assumptions

Given: Moment data, a real symmetric J with distinct ordered eigenvalues, its ith Gaussian weight, and a nonzero candidate vector v and shift θ satisfying the stated residual and gap bounds.

[F1]

Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis: J has an orthonormal eigenbasis u1,…,um with Juk=λkuk.

[F2]

Positive-measure Gaussian quadrature moment data: μ0=μ([a,b])>0.

[F3]

Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square: wi=μ0(ui)12 for a unit eigenvector ui at λi.

[F4]

The Euclidean inner product ⟨x,y⟩=∑k<nxkyk on Rn: on Rm, ∥x∥22=∑k=1mxk2 and each summand is nonnegative.

Proof

1.1givenF1F4algebra

Normalize v^:=v/∥v∥2. Then ∥Jv^−θv^∥2=∥Jv−θv∥2/∥v∥2≤ρ/s. Expand v^=∑k=1mckuk in the orthonormal basis [F1]. Then ∥Jv^−θv^∥22=∑k=1mck2(λk−θ)2≥g2∑k≠ick2. Thus ∑k≠ick2≤ρ2/(s2g2).

2.1givenstep 1.1F1F4algebra

Put ε=1 if ci≥0 and ε=−1 otherwise. Since v^ and ui are unit vectors, ∥v^−εui∥22=2(1−∣ci∣)≤2(1−ci2)=2∑k≠ick2≤2ρ2/(s2g2).

3.1F4step 2.1algebra

By [F4], v^12≤∑k=1mv^k2=1 and (ui)12≤∑k=1m(ui)k2=1, since all coordinate squares are nonnegative. Hence ∣v^1∣,∣(ui)1∣≤1. Therefore ∣v^12−(ui)12∣=∣v^1−ε(ui)1∣ ∣v^1+ε(ui)1∣≤2∥v^−εui∥2≤22 ρ/(sg).

4.1F2F3step 3.1algebra∎

Since v^12=v12/∥v∥22, multiply step 3.1 by μ0>0 from [F2] and use [F3]. The result is the claimed Gaussian weight bound.

AlgorithmAlgorithm: Literature-sourcedProof: Not applicableOpen item page →

Certified Gaussian nodes and weights by Golub–Welsch

Definition

Input an integer m≥1, admissible moment data (a,b,m,r,μ) from Positive-measure Gaussian quadrature moment data with r≥2m−1, finite dyadic intervals I0,…,I2m−1 promised to contain the respective moments μj=∫xj dμ(x), a finite dyadic format, and positive dyadic width tolerances εx,εw. The measure and the truth of the moment-enclosure promise are mathematical input promises; this procedure does not verify them. For the m≥2 proposal branch also supply a finite-return exact projected eigensolver S satisfying the explicit input and output contract of Finite-budget thick and implicitly restarted Arnoldi with residual acceptance and locking. The m=1 branch needs no such provider. Missing or unavailable projected-solver data return inconclusive when that proposal branch is reached; no universal exact eigensolver is constructed here. All interval operations use Outward-rounded interval, rectangle and ball operations. Any overflow, invalid domain, nonpositive divisor, failed solve, undecidable comparison, or failed certification returns inconclusive.

  1. Set p0=1. For each k=1,…,m−1, enclose the Hankel matrix and right-hand side (Hk)ij=μi+j,(bk)i=−μi+k(0≤i,j<k) by the corresponding moment intervals. Take the exact rational midpoints of these interval data and use exact Gaussian elimination with row swaps to the first available nonzero pivot to produce point proposals for Hk−1 and −Hk−1bk. If a pivot is zero, return inconclusive. Round every proposal down to a finite dyadic in the selected format; on overflow, return inconclusive. Call Uniform interval linear residual certificate with inconclusive status on the full rectangle families with these proposals. Continue only on certified, and enclose each coefficient of the unique solution by its reported center plus or minus the outward-rounded uniform error radius. These intervals enclose the coefficients of pk(X)=Xk+∑j<kckjXj whenever the moment promise holds.

  2. Using outward interval arithmetic, compute for 0≤j<m hj=∑r,s=0jcjrcjsμr+s,αj=∑r,s=0jcjrcjsμr+s+1hj, where cjj=1 and p0 has c00=1. For 1≤j<m, also compute βj=hj/hj−1. If any enclosure for hj does not have a strictly positive lower endpoint, or a division or square root is not certified on its domain, return inconclusive. Form a real symmetric interval Jacobi matrix J with diagonal intervals αj and mirrored off-diagonal intervals βj; reuse the same interval in each mirrored pair. Use for certification the family of all real symmetric tridiagonal matrices with those diagonal and mirrored off-diagonal entries and all other entries zero. The moment promise and the preceding enclosures ensure that this family contains the true Jacobi matrix.

  3. If m=1, set the node interval to α0 and the weight interval to I0. Return certified exactly when both widths meet their tolerances and the node interval lies in (a,b); otherwise return inconclusive.

  4. If m≥2, take the interval midpoint matrix Jc and run Finite-budget thick and implicitly restarted Arnoldi with residual acceptance and locking in exact arithmetic on Jc from the first coordinate vector, with full dimension m, target count m−1, restart budget Nmax⁡=1, thick restart mode, and zero Ritz residual tolerance, passing the supplied S to every projected solve. Both projected-solve failure statuses, like every other nonconverged Arnoldi status, return inconclusive here. Supply the following target rule if a restart is reached: scan the current Ritz vectors in the projected eigensolve's returned order, retain the first vectors that increase the span modulo all locked vectors, and stop after selecting the required r−k=m−1−k vectors. If the list supplies too few independent vectors, return unavailable. No shift rule is needed in thick mode. The full-dimension initial cycle is attempted before any restart; the finite restart budget is a fallback, not a success promise. On any status other than converged, or unless the returned locked and current Ritz data together contain m pairs, return inconclusive. Verify that every returned pair has a real Ritz value and exactly zero original residual; otherwise return inconclusive. For each normalized Ritz vector y, if it has complex coordinates replace it by the normalization of the nonzero one of Re⁡y and Im⁡y having larger Euclidean norm. This is still an eigenvector of the real matrix Jc for the same real Ritz value. Round each resulting real Ritz-vector coordinate downward to a finite dyadic and each real Ritz value downward. If the rounded Ritz values are not strictly increasing, return inconclusive; otherwise call the resulting real matrix V and diagonal matrix Θ.

  5. By outward interval operations, calculate a Frobenius upper bound d for V∗V−I, a Frobenius upper bound ρ for JV−VΘ over the entire family J, and Frobenius upper bounds MJ,MΘ for J and Θ. If d≥1, return inconclusive. Compute an outward upper bound τ for 1−1−d. Call Certified Hermitian eigenvalue intervals from residual and Gram bounds for the Hermitian family of Jacobi matrices represented by J, using (V,Θ) and the proposed bounds (d,ρ,MJ,MΘ,τ). Continue only on certified. Let Ni be the returned node intervals in increasing order. Require that they are pairwise disjoint, each lies in (a,b), and each width is at most εx; otherwise return inconclusive.

  6. For each i, let gi be the minimum distance from θi to the other node intervals Nk, k≠i. Require gi>0. Put s equal to the lower endpoint of an outward enclosure of 1−d and require s>0; also require sup⁡(I0)>0. Outward-enclose Ci=I0 V1i2∑j=1mVji2,Ri=22 sup⁡(I0) ρsgi,Wi=Ci+[−Ri,Ri]. The denominator must have a strictly positive lower endpoint. Return certified with (Ni,Wi)i=1m exactly when every Wi is finite and has width at most εw; otherwise return inconclusive. A certified return claims only enclosures, not that the requested tolerances can always be attained.

Soundness interface

Under the input promises, success of each batch 61 interval certifier and sound outward interval evaluation, step 1 encloses the actual orthogonal polynomial coefficients and step 2 encloses the actual Jacobi matrix. The Ritz computation supplies proposals only. The near-orthonormal Hermitian certifier must establish the node intervals for every matrix in that family; the residual-gap lemma then establishes the weight intervals. These are the claims formalized by Certified Golub–Welsch intervals contain the Gaussian nodes and weights ↗.

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

Certified Golub–Welsch intervals contain the Gaussian nodes and weights

Statement

Let admissible moment data (a,b,m,r,μ) with r≥2m−1 and finite moment intervals satisfying the promise in Certified Gaussian nodes and weights by Golub–Welsch be given. If that algorithm returns certified with node intervals Ni and weight intervals Wi, then the Gaussian nodes xi and weights wi satisfy xi∈Ni and wi∈Wi for every i=1,…,m. An inconclusive return makes no claim.

Facts & Assumptions

Given: Admissible moment data, truthful moment enclosures through degree 2m−1, and a certified algorithm return.

[F1]

The uniform interval residual certifier is sound on success: on certified output, each supplied Hankel family has a unique solution and its coefficients lie in the reported error balls.

[F2]

Outward-rounded operations are inclusion-isotone and enclose DAG evaluations: all finite outward interval expression DAGs contain the corresponding exact values.

[F3]

The Gauss orthogonal polynomial has simple roots inside the interval and The Jacobi matrix spectrum is the set of Gauss nodes: the true Jacobi matrix is real symmetric and its increasingly ordered eigenvalues are exactly the Gaussian nodes in (a,b).

[F4]

Certified Hermitian eigenvalue intervals from residual and Gram bounds and The residual and orthogonality certifier encloses the full ordered spectrum: success requires validated residual, Gram and norm bounds, and every increasingly ordered eigenvalue in the admitted Hermitian matrix family lies in its returned node interval.

[F5]

Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square: for a unit eigenvector ui at xi, wi=μ0∣(ui)1∣2.

[F6]

A residual and spectral gap bound a Gaussian weight error: a nonzero approximate eigenvector with norm lower bound s, residual bound ρ, and separation gap lower bound gi gives weight error at most 22 μ0ρ/(sgi).

Proof

1.1F1F2givenalgebra

Each coefficient solve in step 1 uses the full moment rectangle families, which contain the actual Hankel matrix and right-hand side by the moment promise. By [F1], successful uniform residual certification encloses the unique actual coefficient vector. The true Hankel matrices are positive definite by the support hypothesis and The polynomial moment pairing is a real inner product; thus these solutions are the coefficients of the monic orthogonal polynomials. By [F2], the arithmetic in step 2 encloses every true hj,αj,βj and hence the true Jacobi matrix.

2.1F2F3F5step 1.1algebra

If m=1, the true Jacobi matrix is the one-by-one matrix [α0]. By [F3] its sole eigenvalue is the sole Gaussian node x1, so x1=α0. Its unit eigenvector is (1), and [F5] gives w1=μ0. The outward interval α0 and moment interval I0 in step 3 of the algorithm contain these exact values by [F2].

2.2F3F4step 1.1

Assume m≥2. By [F3], the eigenvalues of the true Jacobi matrix are precisely the Gaussian nodes. The matrix family in step 5 contains this matrix by step 1.1, and successful near-orthonormal certification gives xi∈Ni by [F4].

3.1F2F4step 2.2algebra

For column vi of V, the accepted checks in step 5 and [F4] certify ∥Jvi−θivi∥2≤ρ and ∥V∗V−I∥2≤d<1. Therefore ∥vi∥22≥1−d, so the positive lower endpoint s of the outward enclosure of 1−d is a lower bound on its norm. The disjoint ordered intervals in step 5 make gi in step 6 a positive lower bound for the distance from θi to every other true node.

4.1F2F5F6step 3.1algebra

Apply [F6] to (vi,θi) and the bounds of step 3.1. The interval Ci contains μ0(vi)12/∥vi∥22 and Ri is at least the resulting error bound, because sup⁡(I0)≥μ0. Thus the exact Gaussian weight wi belongs to Ci+[−Ri,Ri]=Wi by [F5].

5.1givenstep 2.1step 2.2step 4.1cases∎

The m=1 case is step 2.1; for m≥2, steps 2.2 and 4.1 give every node and weight enclosure. These cases exhaust m≥1, proving the claim.

5 · Examples, counterexamples and false statements

None yet.

Sources