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.
Gaussian quadrature
1 · Prerequisites
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Computed Schur decompositions and nonnormal spectra
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Cyclic Groups and Direct Products
- Darboux, L'Hôpital, and Taylor's Theorem
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Direct Matrix Factorisations: LU, Cholesky and QR
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalue Iterations and the QR Algorithm
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Fundamental Trigonometric Identities
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Krylov Subspaces, Arnoldi and GMRES
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Matrix Norms, Condition Numbers and Numerical Stability
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Outward rounded interval and ball arithmetic
- Polynomial interpolation and remainder
- Polynomial Rings, the Division Algorithm and Roots
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Product Measures and the Fubini Tonelli Theorems
- Relations, Functions, and Quotients
- Restarted and block eigensolvers
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sigma Algebras and Borel Sets
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Lebesgue Integral and the Convergence Theorems
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The Topology of Euclidean Space
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
- Verified linear systems and spectral enclosures
2 · Summary
For a finite positive Borel measure on a compact interval whose support has at least points, the moment pairing is positive definite through degree under the exactness-order moment assumptions, and through degree on the stronger degree- branch. The degree- monic polynomial orthogonal to lower degrees has 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 , and applying exactness to squared cardinal polynomials gives strict positivity.
The moment interface separates two claims. Moments through degree suffice for the node construction and exactness theorem. The Hermite mean-value remainder also needs the degree- moment that makes finite and positive. The proof constructs the degree- polynomial from the positive degree- moment matrix, so it does not assume a degree- 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 nodes for coordinate orders . 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
Positive-measure Gaussian quadrature moment data
Definition
Let be real numbers, let be an integer, and let . A Gaussian quadrature moment datum of order is a finite positive measure on the relative Borel sigma-algebra (The Borel sigma-algebra of a topological space, Measure spaces, Measures on sigma-algebras, Finite, sigma-finite, and semifinite measures) such that , and whose relative support contains at least distinct points. For every integer , require to be integrable with respect to (Integrable real and complex functions, and their integrals) and put . Powers use the natural-exponent recursion of Integer powers , with for every real , including ; thus is the integral of the constant function .
The branch supplies all moments used to construct the monic degree- orthogonal polynomial and prove rule exactness. The stronger branch also defines its squared norm and is used for the remainder formula. Because is bounded and is finite, these absolute-moment integrability conditions follow from finiteness; they are stated to make the two moment interfaces explicit.
Coordinatewise product-moment functional for tensor polynomials
Definition
For , let be Gaussian moment data for each coordinate , and write . A coordinatewise tensor polynomial is a finite family , with for every index, indexed by multi-indices with , evaluated by Define its coordinatewise product-moment value by In both sums, list the finite multi-index box in increasing lexicographic order (coordinate first, then coordinate , and so on), and take the ordinary finite-list sum in that order. The products use increasing coordinate order . 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.
The polynomial moment pairing
Definition
Fix moment data as in Positive-measure Gaussian quadrature moment data. For an integer with and , let with its coordinatewise real vector-space operations. For , let . This is the evaluation of the zero polynomial when , and otherwise of the formal polynomial obtained by deleting trailing zero coefficients from (Formal real polynomials, evaluation, degree, leading coefficient, and monic polynomials). Define the polynomial moment pairing on by 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 ↗.
The polynomial moment pairing is a real inner product
Statement
Let be moment data as in Positive-measure Gaussian quadrature moment data. For with , the pairing The polynomial moment pairing is a well-defined real inner product on .
Facts & Assumptions
Given: Moment data , an integer with and , and coefficient vectors .
Positive-measure Gaussian quadrature moment data: is a finite positive Borel measure; its relative support contains at least points; and is integrable for every .
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero real polynomial of degree at most has at most distinct real roots.
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 for a Borel set .
The Lebesgue integral is linear on : finite sums and real scalar multiples of integrable functions are integrable and their integrals obey the corresponding linearity rules.
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function: every polynomial function is continuous; the relative Borel sigma-algebra is generated by the relative open sets (The Borel sigma-algebra of a topological space).
The reals form a field: real addition and multiplication are distributive and multiplication is commutative.
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
Each monomial with is integrable by [F1]. Expanding the finite coefficient sums in [F2] expresses 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.
Real multiplication is commutative by [F7], so pointwise and the pairing is symmetric.
If , its coefficient list defines a nonzero formal polynomial of degree at most . By [F3], its zero set has at most points. Since the support in [F1] has at least distinct points, choose a support point with .
Put . Continuity in [F6] gives a relative open neighborhood of on which . This set is Borel and has by the support definition. Monotonicity, homogeneity and the indicator identity in [F4] imply
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 by step 2.1. These are the real inner product axioms in [F8].
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 .
Monic orthogonal polynomials satisfy a three-term recurrence
Statement
Let be moment data from Positive-measure Gaussian quadrature moment data with , and let be the monic orthogonal polynomials of Monic orthogonal polynomials exist uniquely through the quadrature order. For put For put . These numbers are defined, , and . The recurrence is and, for , Only moments through degree are used.
Facts & Assumptions
Given: The stated moment data and monic orthogonal family.
Positive-measure Gaussian quadrature moment data: moments through degree are finite.
The polynomial moment pairing is a real inner product: the moment pairing is symmetric, bilinear, and positive definite on each such space.
Monic orthogonal polynomials exist uniquely through the quadrature order: each is monic of degree and orthogonal to every polynomial of smaller degree.
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.
The Lebesgue integral is linear on and The reals form a field: the displayed polynomial integrals are linear and real division by a nonzero norm is defined.
Proof
For , and have degrees at most and , both at most . Thus the moments in and are finite by [F1]. Since is nonzero, [F2] gives , so is defined; for , the same positivity for gives .
The polynomials form a basis for the polynomials of degree at most : their distinct degrees and unit leading coefficients permit successive subtraction of the top term. Because has degree at most by cancellation of the leading term, write it in this basis.
For , symmetry of the real pairing gives , since has degree at most . As the basis in step 1.2 is orthogonal and each squared norm is positive by [F2], the expansion has no term for . Its coefficient is .
For , the coefficient is Indeed, has degree at most , so it is orthogonal to by [F3].
The expansion in step 1.2 therefore has only the displayed and, when , terms. Rearranging it yields the stated recurrence for every .
The Gauss orthogonal polynomial has simple roots inside the interval
Statement
Let be moment data from Positive-measure Gaussian quadrature moment data, with , and let be the monic orthogonal polynomial from Monic orthogonal polynomials exist uniquely through the quadrature order. Then has exactly distinct roots , each simple, and
Facts & Assumptions
Given: The stated moment data and its degree- monic orthogonal polynomial .
Positive-measure Gaussian quadrature moment data: the relative support of contains at least distinct points and all moments through degree are finite.
Monic orthogonal polynomials exist uniquely through the quadrature order: is monic of degree and for every polynomial of degree less than .
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero degree- polynomial has at most distinct real roots.
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 on a Borel set , its integral is at least .
The Lebesgue integral is linear on : the integral is linear on integrable real functions.
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 distinct real numbers is monic of degree .
The image of an interval under a continuous real function is order-convex, hence an interval, and the image of a closed bounded interval is a closed bounded interval: the image of an interval under a continuous real function is an interval.
The reals form a totally ordered field: the real order is total, so an interval in that avoids lies wholly on one side of .
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.
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function: polynomial functions and their finite sums and products are continuous.
Proof
Call an interior root sign-changing if has opposite nonzero signs on the two adjacent intervals containing no root. Let be the set of such roots; it is finite by [F3]. Suppose, for contradiction, that has elements, and put , with the empty product equal to . Between consecutive roots of , the product is continuous and nonzero, so its image is an interval avoiding and its sign is constant by [F7]–[F8]. Crossing a root in flips both factors; crossing any other root of flips neither product sign. Thus has one weak sign on .
Since , orthogonality [F2] gives .
The polynomial has at most distinct roots by [F3]. The support condition [F1] therefore supplies a support point with ; as every root of is a root of , also . Continuity of gives a relative open neighborhood of on which for some and the sign of is unchanged. This neighborhood is Borel and by the definition of support.
If on , [F4] gives . If , apply [F4] to and use [F5] to obtain . Both contradict step 2.1, so .
By [F3], has at most distinct roots, and step 3.1 gives at least , so . Write them as and let . Both and are monic of degree by [F2, F6]. Their degree- coefficients cancel, so their difference is zero or has degree at most by [F9]. It vanishes at all distinct ; if nonzero, [F3] is contradicted. Thus , so each root occurs as exactly one linear factor and is simple.
Double-node Hermite interpolation has the stated pointwise remainder
Statement
Let , let , let lie in , and let . Let be the Lagrange cardinal polynomials for these nodes as in Cardinal-polynomial interpolation. Define Then has degree at most , and and for every . If is not a node, there is such that where . At a node the interpolation error and the displayed node product are both zero.
Facts & Assumptions
Given: Distinct interior nodes , , and a query .
Cardinal-polynomial interpolation and Cardinal interpolation functions are polynomials of the stated degrees: is a polynomial of degree at most , when and otherwise, and the node product is nonzero off the nodes.
Higher derivatives and the classes and : all derivatives of through order exist on and are continuous there.
Sums, scalar multiples, products and quotients: , , , and when : finite sums, scalar multiples and products obey the derivative rules on the interval.
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function: polynomials and their finite sums and products are continuous.
Higher-order Rolle theorem: a function with distinct ordered zeros, continuous on their closed span and -times differentiable in its interior, has an th-derivative zero between the extreme zeros.
A finite product of monic linear factors is monic: the product of monic linear factors is monic of degree .
The highest derivative of a polynomial: a polynomial of degree at most has constant th derivative equal to times its coefficient of .
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
For each , set At , , so , , , and by the product rule [F3]. At with , , so both and its derivative vanish there; hence . Therefore the displayed sum defining satisfies every value and first-derivative condition.
Each has degree at most by [F1]. Thus has degree at most , and multiplication by an affine polynomial gives degree at most by [F8]. The finite sum also has degree at most .
Suppose is not a node. By [F1], , so put and define . At every node, step 1.1 and give and , since by [F3]; by the choice of , also .
Sort the nodes and as . Rolle's theorem on each consecutive interval gives a zero of strictly between its endpoints; these zeros are distinct and are not nodes. Along with at the nodes from step 2.1, this gives distinct ordered zeros of .
The function is continuous on the closed span of those zeros and is -times differentiable on its interior by [F2]–[F4]. Apply [F5] to those zeros. There is with .
By step 1.2, . By [F6], is monic of degree , so [F7] gives . Differentiating the definition of in step 2.1 at therefore gives .
By [F9], , so step 5.1 gives . Substituting this into the definition of in step 2.1 proves the remainder formula. At a node, step 1.1 gives zero interpolation error and .
The m-node Gaussian quadrature rule for a positive measure
Definition
Let be moment data as in Positive-measure Gaussian quadrature moment data. Let be its monic degree- orthogonal polynomial and write its ordered roots as , as established in The Gauss orthogonal polynomial has simple roots inside the interval. For , define the Lagrange cardinal polynomial using the convention of Cardinal-polynomial interpolation. Define the Gaussian quadrature weights and the m-node Gaussian rule by for any real-valued function 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 ↗.
The Jacobi matrix spectrum is the set of Gauss nodes
Statement
Let be moment data from Positive-measure Gaussian quadrature moment data with . For , let be as in Monic orthogonal polynomials satisfy a three-term recurrence, and for let . Put and define the real symmetric tridiagonal Jacobi matrix with the evident one-by-one interpretation for . If are the roots of , then , where spectrum denotes the set of eigenvalues as in Eigenvalues, eigenvectors, eigenspaces , and the spectrum of an endomorphism; every eigenvalue is simple. If for , then is a nonzero eigenvector of for .
Facts & Assumptions
Given: Moment data and the monic orthogonal polynomials through degree .
The Gauss orthogonal polynomial has simple roots inside the interval: the degree- polynomial has exactly distinct roots .
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.
The standard formulas on and on are inner products and In an orthonormal basis, self-adjoint means conjugate-transpose symmetry and normal means commuting with the conjugate transpose: a real symmetric matrix is self-adjoint for the standard Euclidean inner product on .
Nonnegative real square roots exist uniquely: every positive real has a unique positive square root.
Eigenvalues, eigenvectors, eigenspaces , and the spectrum of an endomorphism: the spectrum of a finite-dimensional endomorphism is its set of eigenvalues.
Proof
By [F1] and [F5], the normalized polynomials satisfy for , with . For the final index, the monic recurrence gives ; at a root the term vanishes. After dividing by , this is the final row of for . When , the identity gives the same conclusion at the sole root.
The vector is nonzero because and . Thus each of the distinct roots in [F2] is an eigenvalue of .
The matrix is real symmetric by its definition, so [F4] makes its endomorphism self-adjoint on . By [F3], it has an orthonormal eigenbasis of vectors; hence it has exactly eigenvalues counted with multiplicity. The 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 .
Consecutive Gaussian orthogonal polynomials and Jacobi truncations strictly interlace
Statement
Let be Gaussian moment data with and monic orthogonal polynomials . For every , write the roots of as and those of as . Then Equivalently, the spectra of consecutive leading principal Jacobi truncations strictly interlace. If , there is no pair of positive consecutive degrees and the assertion is vacuous.
Facts & Assumptions
Given: The moment data and its monic orthogonal family.
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 , retaining moments through ; uniqueness identifies each shorter orthogonal family with the prefix .
The Gauss orthogonal polynomial has simple roots inside the interval gives simple roots in and the monic factorization of for each .
Monic orthogonal polynomials satisfy a three-term recurrence gives with for , and .
The image of an interval under a continuous real function is order-convex, hence an interval, and the image of a closed bounded interval is a closed bounded interval and Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function imply that a real polynomial taking opposite signs at two points has a root strictly between them.
The reals form a totally ordered field gives the sign rules for the finite products in the monic factorization.
The Jacobi matrix spectrum is the set of Gauss nodes identifies the spectrum of with the roots of ; its coefficients are the same prefix recurrence coefficients.
Proof
The reduced-order uses in [F1] are valid: the original support has at least points and the absolute moments through are among those assumed. By [F2], every has the stated simple roots and monic factorization. If , there is no pair to prove. For the remaining case , take ; if is the root of , [F3] gives . At both and , the degree-two monic factorization [F2] gives and . By [F4] there is one root on each side of inside , proving the first pair when .
For , assume the roots of strictly interlace those of . At , exactly roots of the monic polynomial lie to its right and none equals it. Its factorization [F2] therefore makes its sign . By [F3], the sign of is . These signs alternate, so [F4] supplies a root in each . Also [F2] gives sign for and positive sign for ; these are opposite to the signs at and at . Thus [F4] supplies an additional root in each of and .
These disjoint intervals contain distinct roots, and [F2] says there are exactly that many. They therefore exhaust the roots of in the required order. Starting with step 1.1, finite induction through step 1.2 proves every pair through . 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 the quantified pair set is empty.
The Gaussian nodes, cardinal polynomials and weights are well-defined
Statement
For moment data with order , 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 , every cardinal polynomial is defined, every weight integral is finite, and is a finite real number whenever is real-valued at the nodes.
Facts & Assumptions
Given: Moment data and the rule of The m-node Gaussian quadrature rule for a positive measure.
Positive-measure Gaussian quadrature moment data: moments through degree are finite.
Monic orthogonal polynomials exist uniquely through the quadrature order and The Gauss orthogonal polynomial has simple roots inside the interval: is monic and has exactly distinct roots in .
Cardinal-polynomial interpolation: for distinct nodes, ; every denominator is a nonzero node difference.
Cardinal interpolation functions are polynomials of the stated degrees: each is a polynomial of degree at most and has the cardinal values at the nodes.
The Lebesgue integral is linear on : a finite linear combination of integrable monomials is integrable and its integral is the corresponding finite linear combination of moments.
Proof
By [F2], the roots of form a finite set of exactly distinct points in , so sorting them defines without ambiguity. Their pairwise differences are nonzero, so [F3] defines every . By [F4], each cardinal polynomial has degree at most .
Each is a finite linear combination of monomials of degrees at most . These moments are finite by [F1], so [F5] makes every finite.
For any real values , the expression has finitely many finite real summands, hence is a defined real number. This establishes the stated well-definedness of .
Tensor-product Gaussian quadrature
Definition
Let and, for each coordinate , let 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 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.
Gaussian quadrature is exact through degree 2m−1 and has positive weights
Statement
Let be moment data as in Positive-measure Gaussian quadrature moment data, and let and be the rule and weights of The m-node Gaussian quadrature rule for a positive measure. For every real polynomial of degree at most , Moreover, for every and .
Facts & Assumptions
Given: Moment data, its -node Gaussian rule, and a real polynomial of degree at most .
Positive-measure Gaussian quadrature moment data: all moments through degree are finite.
Monic orthogonal polynomials exist uniquely through the quadrature order: is monic of degree and is orthogonal to all polynomials of degree less than .
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 , and the rule's weights and cardinal polynomials are defined.
Division by a monic polynomial over a commutative ring: with either or .
Cardinal-polynomial interpolation, Cardinal interpolation functions are polynomials of the stated degrees, and Existence and uniqueness of the cardinal-polynomial interpolant: every polynomial of degree at most satisfies .
The polynomial moment pairing is a real inner product: a nonzero polynomial of degree at most has strictly positive squared integral.
The Lebesgue integral is linear on : the integral distributes over the finite polynomial sums used below.
Proof
By [F4], write , where or . If , the leading term of has degree and cannot cancel against , so and . The moments in these polynomials are finite by [F1]. If , the integral of is zero; otherwise orthogonality [F2] gives . Thus by [F8].
At every node , , so and [F5] gives .
The cardinal interpolation identity [F6] gives . Integrating this finite sum and using [F5] and [F8] yields
Steps 1.1–1.3 give , proving exactness for every such .
For a fixed , has degree at most , so step 2.1 applied to this polynomial gives by the cardinal values in [F6]. The cardinal polynomial is nonzero because ; [F7] therefore gives .
Applying exactness to the constant polynomial and then [F9] gives .
Gaussian quadrature has the Hermite mean-value remainder
Statement
Let be moment data as in Positive-measure Gaussian quadrature moment data, let be its Gaussian rule, and let . Then there is such that where is the monic node polynomial and .
Facts & Assumptions
Given: Moment data of order , its Gaussian rule, and a real function .
Positive-measure Gaussian quadrature moment data: is finite and positive, its support contains at least points, and moments through degree are finite.
The m-node Gaussian quadrature rule for a positive measure and Gaussian quadrature is exact through degree 2m−1 and has positive weights: is the finite Gaussian rule, is exact through degree , and its nodes are the roots of .
Double-node Hermite interpolation has the stated pointwise remainder: for the nodes , the explicit polynomial has degree at most , matches at the nodes, and for every non-node the pointwise remainder is for some .
The polynomial moment pairing is a real inner product: since , is finite and strictly positive.
Extreme value theorem: a continuous real function on a nonempty compact subset of attains a greatest and a least value and The image of an interval under a continuous real function is order-convex, hence an interval, and the image of a closed bounded interval is a closed bounded interval: a continuous real function on has a minimum and maximum and takes every value between them.
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: nonnegative integrals are monotone and homogeneous, and .
The Lebesgue integral is linear on : integrable functions may be added and integrated linearly.
Continuity of at a point of and on : the - condition, its agreement with at a limit point, and continuity at an isolated point and The Borel sigma-algebra of a topological space: a continuous function on is Borel measurable, since preimages of relative open sets are relative open and those sets generate .
Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function: absolute values, polynomial functions, sums and products of the continuous functions used below are continuous.
Proof
The function and the polynomial from [F3] are continuous, hence Borel measurable by [F9]–[F10]. They are bounded on the compact interval by [F5]. Since by [F1], monotonicity and homogeneity in [F6] give and . Also is integrable by [F4]. Thus all integrals below are finite.
Let and , which exist by [F5]. The pointwise remainder [F3], and the zero error at nodes, imply for every that The multiplier is nonnegative.
By [F3], and . Exactness [F2] gives , so the quadrature error is .
Integrating the nonnegative differences in step 1.2 and using [F6]–[F7] gives By [F4], . Define ; the displayed bounds give .
If , is constant and any point of has value . If , choose points attaining and by [F5]. They are distinct; the intermediate value property on the segment between them gives a point strictly between the extrema where , and that point lies in .
Suppose and has no interior point with value . Then for every . For every non-node , [F3] uses some , and so because . At nodes, . There is a support point not among the nodes by [F1]; continuity makes on a relative open neighborhood of , and . Thus [F6] gives . But and step 2.2 imply , a contradiction. Hence some interior point has derivative value .
The case is analogous: if throughout , the nonnegative continuous function is strictly positive at every non-node, hence on a positive-measure neighborhood of a support point outside the nodes. This contradicts in step 2.2. Therefore there is an interior point with derivative value .
In every case there is with . Substitute the definition of , use step 2.1, and rearrange to obtain the stated remainder formula.
Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square
Statement
Let be moment data and let have nodes and weights as in The m-node Gaussian quadrature rule for a positive measure. Let be the Jacobi matrix of The Jacobi matrix spectrum is the set of Gauss nodes, and let be any unit eigenvector of for eigenvalue . Then
Facts & Assumptions
Given: Moment data, its Gaussian rule, its Jacobi matrix, and a unit eigenvector for node .
The m-node Gaussian quadrature rule for a positive measure: and the nodes are the roots of .
Gaussian quadrature is exact through degree 2m−1 and has positive weights: the rule is exact through degree and .
Monic orthogonal polynomials exist uniquely through the quadrature order and Monic orthogonal polynomials satisfy a three-term recurrence: , the are pairwise orthogonal, and their squared norms are positive.
The Jacobi matrix spectrum is the set of Gauss nodes: the vectors are nonzero eigenvectors of with eigenvalue , the nodes are distinct, and every eigenvalue is simple.
Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis: a real self-adjoint endomorphism has an orthonormal eigenbasis.
Cardinal interpolation functions are polynomials of the stated degrees: has degree at most and .
The polynomial moment pairing and Monic orthogonal polynomials satisfy a three-term recurrence define . The integral of a nonnegative simple function and The nonnegative integral agrees with the simple integral on simple functions give by the one-cell simple function on the whole interval.
Proof
Put for . By [F3], they are orthonormal. Their degrees are with nonzero leading coefficients, so successive subtraction of top terms shows they span the degree-at-most- polynomial space and are a basis. For a fixed node , set . By [F4], is a nonzero eigenvector of for .
For , self-adjointness gives . Since the nodes are distinct by [F4], their difference is nonzero and the pairing is zero. Normalize these vectors to ; their first coordinates satisfy The simplicity assertion in [F4], together with the orthonormal eigenbasis [F5], makes each eigenspace one-dimensional. Therefore the arbitrary unit eigenvector in the Statement is or , and its first-coordinate square is the same.
Expand in the orthonormal basis . Its coefficient of is because and by [F6].
By [F6], has degree at most and , so exactness [F2] gives . Taking squared norms of the expansion in step 1.3 gives Since by [F2], it follows that .
Since by [F3], [F7] gives ; thus one has . Substituting this and step 2.1 into step 1.2 yields , which is the claimed formula.
Tensor-product Gaussian quadrature preserves coordinatewise exactness
Statement
For , take one-dimensional Gaussian rules with orders and moment data , and form their tensor rule as in Tensor-product Gaussian quadrature. For every coordinatewise tensor polynomial of Coordinatewise product-moment functional for tensor polynomials, The tensor rule has exactly distinct nodes. In particular, using the same nodes in each coordinate requires node evaluations, exponential growth in .
Facts & Assumptions
Given: The finite family of one-dimensional Gaussian rules and a coordinatewise tensor polynomial.
Tensor-product Gaussian quadrature: the tensor rule sums over all node tuples and multiplies the one-dimensional weights.
Coordinatewise product-moment functional for tensor polynomials: acts on a monomial by the product of its one-dimensional moments and extends by finite linearity.
Gaussian quadrature is exact through degree 2m−1 and has positive weights: each coordinate rule is exact on every monomial of degree at most .
Finite sums and finite products, by recursion, The product of a finite list in a monoid, by recursion, with the empty product () equal to the identity, and Laws of finite sums and finite products: finite sums distribute over finite products and finite products obey their recursive product rule.
The reals form a field: multiplication and finite sums obey distributivity.
Proof
For a coordinate monomial , substitute the rule definition [F1] and distribute the finite sums:
Since , [F3] makes the th factor in step 1.1 equal to . Their product is exactly by [F2].
A coordinatewise tensor polynomial is a finite linear combination of these monomials. Both and are linear finite sums, so step 2.1 extends term by term and proves .
Each one-dimensional node list has 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 tuples for ; appending coordinate replaces each prior tuple by exactly distinct extensions. Iterating this finite recurrence gives exactly nodes, hence when every .
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 on the finite product of the relative Borel interval spaces, with binary products iterated from left to right. Every real coordinatewise tensor polynomial in that definition is integrable and 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: , the finite positive coordinate moment measures and the real finite polynomial coefficients.
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.
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.
Tonelli's theorem for nonnegative measurable functions on a sigma-finite product factors iterated integrals of nonnegative product-measurable functions.
Fubini's theorem for L^1 functions on a sigma-finite product gives iterated integration for integrable functions; The Lebesgue integral is linear on gives scalar and finite-sum linearity.
The Borel product of R^m and R^n is the Borel sigma-algebra of R^{m+n}, The Borel sigma-algebra of a subspace is the trace of the ambient Borel sigma-algebra, and A continuous map has Borel preimages of Borel sets give product measurability of restrictions of continuous real coordinate polynomials; Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function gives their continuity.
Proof
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 -factor case. Hence each coordinate monomial and its absolute value are product-measurable by [F5].
Fix an allowed multi-index . Apply [F3] successively to the nonnegative function . 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 . Thus the signed real monomial is integrable.
Apply [F4] to that integrable monomial, integrating the last coordinate first. Its one-variable signed moment is ; repeat for the preceding coordinates. Each use is legitimate by the absolute integrability in step 2.1, and finite induction yields . At this is the moment definition itself.
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 . No probability normalization or new choice assumption was used.
A residual and spectral gap bound a Gaussian weight error
Statement
Let be moment data. Put . Let be the real symmetric Jacobi matrix of The Jacobi matrix spectrum is the set of Gauss nodes, with distinct eigenvalues and Gaussian weights of Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square. Fix , a nonzero vector , a real number , and . Suppose and For ,
Facts & Assumptions
Given: Moment data, a real symmetric with distinct ordered eigenvalues, its th Gaussian weight, and a nonzero candidate vector and shift satisfying the stated residual and gap bounds.
Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis: has an orthonormal eigenbasis with .
Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square: for a unit eigenvector at .
The Euclidean inner product on : on , and each summand is nonnegative.
Proof
Normalize . Then . Expand in the orthonormal basis [F1]. Then Thus .
Put if and otherwise. Since and are unit vectors, .
By [F4], and , since all coordinate squares are nonnegative. Hence . Therefore
Since , multiply step 3.1 by from [F2] and use [F3]. The result is the claimed Gaussian weight bound.
Certified Gaussian nodes and weights by Golub–Welsch
Definition
Input an integer , admissible moment data from
Positive-measure Gaussian quadrature moment data with , finite dyadic intervals
promised to contain the respective moments
, a finite dyadic format, and positive dyadic
width tolerances . The measure and the truth of
the moment-enclosure promise are mathematical input promises; this procedure
does not verify them. For the proposal branch also supply a
finite-return exact projected eigensolver satisfying the explicit input
and output contract of Finite-budget thick and implicitly restarted Arnoldi with residual acceptance and locking.
The 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.
-
Set . For each , enclose the Hankel matrix and right-hand side 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 and . If a pivot is zero, return
inconclusive. Round every proposal down to a finite dyadic in the selected format; on overflow, returninconclusive. Call Uniform interval linear residual certificate with inconclusive status on the full rectangle families with these proposals. Continue only oncertified, 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 whenever the moment promise holds. -
Using outward interval arithmetic, compute for where and has . For , also compute . If any enclosure for 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 with diagonal intervals and mirrored off-diagonal intervals ; 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. -
If , set the node interval to and the weight interval to . Return
certifiedexactly when both widths meet their tolerances and the node interval lies in ; otherwise returninconclusive. -
If , take the interval midpoint matrix and run Finite-budget thick and implicitly restarted Arnoldi with residual acceptance and locking in exact arithmetic on from the first coordinate vector, with full dimension , target count , restart budget , thick restart mode, and zero Ritz residual tolerance, passing the supplied to every projected solve. Both projected-solve failure statuses, like every other nonconverged Arnoldi status, return
inconclusivehere. 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 vectors. If the list supplies too few independent vectors, returnunavailable. 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 thanconverged, or unless the returned locked and current Ritz data together contain pairs, returninconclusive. Verify that every returned pair has a real Ritz value and exactly zero original residual; otherwise returninconclusive. For each normalized Ritz vector , if it has complex coordinates replace it by the normalization of the nonzero one of and having larger Euclidean norm. This is still an eigenvector of the real matrix 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, returninconclusive; otherwise call the resulting real matrix and diagonal matrix . -
By outward interval operations, calculate a Frobenius upper bound for , a Frobenius upper bound for over the entire family , and Frobenius upper bounds for and . If , return
inconclusive. Compute an outward upper bound for . Call Certified Hermitian eigenvalue intervals from residual and Gram bounds for the Hermitian family of Jacobi matrices represented by , using and the proposed bounds . Continue only oncertified. Let be the returned node intervals in increasing order. Require that they are pairwise disjoint, each lies in , and each width is at most ; otherwise returninconclusive. -
For each , let be the minimum distance from to the other node intervals , . Require . Put equal to the lower endpoint of an outward enclosure of and require ; also require . Outward-enclose The denominator must have a strictly positive lower endpoint. Return
certifiedwith exactly when every is finite and has width at most ; otherwise returninconclusive. 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 ↗.
Certified Golub–Welsch intervals contain the Gaussian nodes and weights
Statement
Let admissible moment data with 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 and weight intervals
, then the Gaussian nodes and weights satisfy
and for every . An inconclusive
return makes no claim.
Facts & Assumptions
Given: Admissible moment data, truthful moment enclosures through degree , and a certified algorithm return.
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.
Outward-rounded operations are inclusion-isotone and enclose DAG evaluations: all finite outward interval expression DAGs contain the corresponding exact values.
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 .
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.
Gaussian weights equal the total mass times a Jacobi eigenvector's first-coordinate square: for a unit eigenvector at , .
A residual and spectral gap bound a Gaussian weight error: a nonzero approximate eigenvector with norm lower bound , residual bound , and separation gap lower bound gives weight error at most .
Proof
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 and hence the true Jacobi matrix.
If , the true Jacobi matrix is the one-by-one matrix . By [F3] its sole eigenvalue is the sole Gaussian node , so . Its unit eigenvector is , and [F5] gives . The outward interval and moment interval in step 3 of the algorithm contain these exact values by [F2].
Assume . 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 by [F4].
For column of , the accepted checks in step 5 and [F4] certify and . Therefore , so the positive lower endpoint of the outward enclosure of is a lower bound on its norm. The disjoint ordered intervals in step 5 make in step 6 a positive lower bound for the distance from to every other true node.
Apply [F6] to and the bounds of step 3.1. The interval contains and is at least the resulting error bound, because . Thus the exact Gaussian weight belongs to by [F5].
The case is step 2.1; for , steps 2.2 and 4.1 give every node and weight enclosure. These cases exhaust , proving the claim.
5 · Examples, counterexamples and false statements
None yet.
Sources
- John A. Gubner, Gaussian Quadrature and the Eigenvalue Problem, §§2–5, 2020
- MATH 373 Lecture Notes, Lecture 14: Gaussian Quadrature, §§10.2–10.5
- Walter Gautschi, The Interplay Between Classical Analysis and (Numerical) Linear Algebra, §2.1
- Gautschi, The Interplay Between Classical Analysis and (Numerical) Linear Algebra, §2.1
- Tao, An Introduction to Measure Theory, §1.7