Lagrange polynomial

Not to be confused with Legendre polynomials (the orthogonal basis of function space).

This image shows, for four data points ((−9, 5), (−4, 2), (−1, −2), (7, 9)), the (cubic) interpolation polynomial L(x) (dashed, black), which is the sum of the scaled basis polynomials y₀ℓ₀(x), y₁ℓ₁(x), y₂ℓ₂(x) and y₃ℓ₃(x). The interpolation polynomial passes through all four control points, and each scaled basis polynomial passes through its respective control point and is 0 where x corresponds to the other three control points.

In numerical analysis, the Lagrange interpolating polynomial is the unique polynomial of lowest degree that interpolates a given set of data.

Given a data set of coordinate pairs ⁠$\textstyle (x_{j},y_{j})$⁠, the ⁠$\textstyle x_{j}$⁠ are called nodes and the ⁠$\textstyle y_{j}$⁠ are called values. The Lagrange polynomial ⁠$L(x)$⁠ which interpolates the data assumes each value at the corresponding node, ⁠$\textstyle L(x_{j})=y_{j}$⁠. If there are ⁠$k+1$⁠ data pairs, the Lagrange polynomial has degree ⁠$\leq k$⁠.

Although named after Joseph-Louis Lagrange, who published it in 1795, the method was first discovered in 1779 by Edward Waring. It is also an easy consequence of a formula published in 1783 by Leonhard Euler.

Uses of Lagrange polynomials include the Newton–Cotes method of numerical integration, Shamir's secret sharing scheme in cryptography, and Reed–Solomon error correction in coding theory.

For equispaced nodes, Lagrange interpolation is susceptible to Runge's phenomenon of large oscillation.