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Existence and uniqueness of the cardinal-polynomial interpolant
Statement
Let , , let be distinct real nodes, and let . Then from Cardinal-polynomial interpolation is the unique real polynomial of degree at most satisfying for every .
Facts & Assumptions
Given: A natural number , distinct real nodes , and real data .
Cardinal interpolation functions are polynomials of the stated degrees: is the evaluation of a formal polynomial of degree at most , and for and otherwise.
A finite coefficient vector has a canonical polynomial representative: a coefficient vector through index represents a formal polynomial of degree at most , whose representative is zero exactly when all coefficients vanish.
A nonzero real polynomial of degree has no more than distinct real roots: a nonzero real polynomial of degree has at most distinct real roots.
Laws of finite sums and finite products and The reals form a field: coefficientwise subtraction distributes over the finite evaluation sums, so the vector evaluates to .
Proof
By [F2], choose a formal polynomial of degree at most (allowing the zero polynomial) whose evaluation equals the function for every real . At node , [F1, F2] give . Thus the formal polynomial is an interpolant.
Let be any formal polynomial of degree at most (allowing zero) with , and denote its evaluation by . Extend the actual coefficient lists of and by zeros through index (using all-zero vectors for zero polynomials), obtaining vectors and ; these describe their evaluations and , not padded formal objects. The vector defines by [F3] a formal polynomial of degree at most ; [F5] gives for every real . Since and agree at all nodes by step 1.1 and the choice of , has the distinct roots . If were nonzero, [F4] would give at most roots, a contradiction. Hence is the zero formal polynomial; by [F3], every . The canonical coefficient lists of and are therefore identical (or both objects are zero), so , and consequently as evaluation functions.
This proves that the interpolant from step 1.1 is the unique formal polynomial of degree at most satisfying the node values.
Depends on
- Cardinal-polynomial interpolation
- Cardinal interpolation functions are polynomials of the stated degrees
- A finite coefficient vector has a canonical polynomial representative
- A nonzero real polynomial of degree $n$ has no more than $n$ distinct real roots
- Laws of finite sums and finite products
- The reals form a field
Used by
- Chebyshev–Gauss–Lobatto polynomial differentiation matrix Algorithm
- Chebyshev–Gauss–Lobatto collocation grid Definition
- A cubic gives equality in the pointwise interpolation remainder Example
- Quadratic interpolation at three nodes is exact Example
- Chebyshev cardinal interpolation reproduces polynomials and its norm is the cardinal sum Lemma
- Chebyshev–Lobatto interpolation folds polynomial modes Lemma
- Collocation polynomial derivatives scale with the step size Lemma
- Divided differences are symmetric leading coefficients Lemma
- Exact weights and degree-nine exactness of the eight-panel rule Lemma
- Floater–Hormann window polynomials interpolate their local data Lemma
- Newton remainder for a Floater–Hormann window polynomial Lemma
- The Gaussian nodes, cardinal polynomials and weights are well-defined Lemma
- The selected nodal ENO2 quadratic reconstructs the face to third order Lemma
- Barycentric interpolation identities Theorem
- Chebyshev–Gauss–Lobatto differentiation is exact on the interpolant Theorem
- DCT-I coefficients of the Chebyshev–Lobatto interpolant Theorem
- Double-node Hermite interpolation has the stated pointwise remainder Theorem
- Gaussian quadrature is exact through degree 2m−1 and has positive weights Theorem
- M-node collocation has local defect order M Theorem
- Pointwise Lagrange interpolation remainder Theorem
Dependency tree · two levels
33 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
- Mark Embree, Lecture Notes on Numerical Analysis, lecture 1 (standard reference, not scraped)
- Jean-Paul Berrut and Lloyd N. Trefethen, Barycentric Lagrange Interpolation (standard reference, not scraped)