How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Monic orthogonal polynomials exist uniquely through the quadrature order
Statement
Let be moment data as in Positive-measure Gaussian quadrature moment data, with and . For each there is a unique monic real polynomial of degree such that for every real polynomial of degree less than . For , this means and the orthogonality condition is vacuous. The polynomial uses moments only through degree ; its squared norm is asserted only when the moment datum has order .
Facts & Assumptions
Given: Moment data with .
Positive-measure Gaussian quadrature moment data: is finite and positive, its relative support has at least points, and every absolute moment through order is finite.
The polynomial moment pairing is a real inner product: for , the coefficient-vector pairing is a real inner product.
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.
The standard list with and for is an ordered basis of ; hence , and is the zero space with basis and dimension and The standard formulas on and on are inner products: the standard vectors form a basis of .
Rank-nullity: : for a linear map from a finite-dimensional vector space to itself, trivial kernel implies full-dimensional image and hence surjectivity.
The Lebesgue integral is linear on : finite polynomial expansions may be integrated term by term whenever the displayed moments are finite.
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.
The reals form a field: every nonzero real coefficient has a multiplicative inverse.
Proof
By [F2], the pairing on the coefficient space for degrees at most is an inner product. The list is independent by [F4], so [F3] gives orthonormal coefficient vectors whose first vectors span the same space as . The polynomial therefore has degree exactly : it lies in the span of but not in the span through degree . Dividing by its nonzero leading coefficient, which is invertible by [F8], gives a monic , orthogonal to every polynomial of degree less than .
Put for , and . All entries are finite by [F1]. The matrix represents the inner product of [F2] in the standard basis. If , then , so [F2] gives . Thus the linear map has trivial kernel; [F5] makes it surjective. There is therefore a unique coefficient vector solving .
Define . Its leading coefficient is , and the th equation of gives for each . Linearity [F6] extends this equality to every polynomial of degree less than . If another monic polynomial had the same orthogonality, its difference from would have degree at most and pair to zero with itself; positive definiteness [F2] makes that difference zero.
Step 1.1 gives the unique monic polynomials below degree , and steps 1.2–2.1 give the unique degree- polynomial using only moments through . This proves the assertion for every .
Depends on
- Positive-measure Gaussian quadrature moment data
- The polynomial moment pairing
- The polynomial moment pairing is a real inner product
- Gram–Schmidt turns every finite independent list into an orthonormal list with the same successive spans
- Rank-nullity: $\dim_F V=\operatorname{nullity}T+\operatorname{rank}T$
- A finite coefficient vector has a canonical polynomial representative
- Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials
- The reals form a field
- The standard list $e : n \to F^{n}$ with $e_i(i) = 1_F$ and $e_i(j) = 0_F$ for $j \ne i$ is an ordered basis of $F^{n}$; hence $\dim_F F^{n} = n$, and $F^{0}$ is the zero space with basis $\varnothing$ and dimension $0$
- The standard formulas $\langle x,y\rangle=\sum_{k<n}x_k y_k$ on $\mathbb R^n$ and $\sum_{k<n}x_k\overline{y_k}$ on $\mathbb C^n$ are inner products
- The Lebesgue integral is linear on $L^1(\mu)$
Used by
- Certified Gaussian nodes and weights by Golub–Welsch Algorithm
- The m-node Gaussian quadrature rule for a positive measure Definition
- Weighted orthogonal-polynomial Galerkin spaces and spectral scale Definition
- Gaussian and Simpson mass assembly give distinct exact and approximate checks Example
- The two-node Gauss–Legendre rule is exact through degree three Example
- Two-node Gauss is exact through degree three but not for x to the fourth Example
- A Gaussian collocation defect has a high-order weighted endpoint bound Lemma
- Monic orthogonal polynomials satisfy a three-term recurrence Lemma
- One-node Gauss data are midpoint data Lemma
- The Gaussian nodes, cardinal polynomials and weights are well-defined Lemma
- Certified Golub–Welsch intervals contain the Gaussian nodes and weights Theorem
- Consecutive Gaussian orthogonal polynomials and Jacobi truncations strictly interlace Theorem
- Gaussian quadrature exactly assembles the orthogonal Galerkin mass matrix Theorem
- Gaussian quadrature is exact through degree 2m−1 and has positive weights Theorem
- Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square Theorem
- The Gauss orthogonal polynomial has simple roots inside the interval Theorem
- The Jacobi matrix spectrum is the set of Gauss nodes Theorem
Dependency tree · two levels
63 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- John A. Gubner, Gaussian Quadrature and the Eigenvalue Problem, §§2–5, 2020 (standard reference, not scraped)
- MATH 373 Lecture Notes, Lecture 14: Gaussian Quadrature, §§10.2–10.5 (standard reference, not scraped)
- Walter Gautschi, The Interplay Between Classical Analysis and (Numerical) Linear Algebra, §2.1 (standard reference, not scraped)