Linked from

The 24 pages that link to Polynomial interpolation, each with the reason it gives.

• PolynomialRelated: A polynomial can reconstruct a curve passing through a finite set of observations.

• Numerical stabilityRelated: Poor node choices or formulas can cause large numerical errors despite exact interpolation in theory.

• InterpolationBroader topic: A single polynomial can pass exactly through every supplied point.

• Numerical integrationRelated: Many quadrature rules integrate an interpolating polynomial instead of the original function.

• Chebyshev polynomialsNarrower topic: Chebyshev polynomials guide the choice of interpolation points that limits worst-case error.

• Polynomial equationRelated: Interpolation determines unknown polynomial coefficients by solving simultaneous equations.

• Polynomial degreeRelated: With distinct nodes, n data values determine an interpolating polynomial of degree at most n−1.

• Polynomial functionRelated: Interpolation uses polynomial functions to pass exactly through a finite set of data points.

• Rational approximationCompared with: Unlike rational interpolation, it cannot represent poles through its denominator.

• Reed–Solomon codeRelated: Decoding reconstructs the transmitted polynomial from sufficiently many correct evaluation points.

• Spectral methodRelated: Collocation spectral methods build global approximants through interpolation.

• Weierstrass approximation theoremCompared with: The theorem promises uniform approximation, while interpolation alone need not converge uniformly.

• Lagrange interpolationNarrower topic: Lagrange interpolation is an explicit formula within this broader family of methods.

• Linear approximationCompared with: It fits selected values, whereas linear approximation is fixed by local derivative data.

• Polynomial evaluationRelated: Interpolation repeatedly evaluates candidate polynomials to match given observations.

• Approximation theoryRelated: Interpolation turns sampled function values into a continuous approximant, though node choice controls its error.

• Horner's methodRelated: Interpolating polynomials are often evaluated efficiently in nested form.

• Newton's identitiesRelated: The identities can constrain coefficients when root power sums are known.

• Pafnuty ChebyshevRelated: Chebyshev nodes help control interpolation error and avoid severe oscillation.

• Polynomial remainder theoremRelated: Remainder conditions encode prescribed values such as p(a) = r.

• Cayley–Bacharach theoremCompared with: Ordinary interpolation suggests independent point conditions; Cayley–Bacharach forces one condition to be dependent.

• Markov brothers' inequalityRelated: Derivative bounds help analyze how interpolation polynomials vary between data points.

• Reed–Solomon error correctionRelated: Decoding recovers a polynomial from surviving symbol values and their positions.

• Adi ShamirRelated: Shamir's threshold scheme reconstructs its secret by interpolating a polynomial.