Lagrange Interpolation

Interpolating Polynomial

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Lagrange Interpolation

Lagrange interpolation is used to find a unique polynomial of least degree that best fits a set of distinct data points. To find the polynomial that passes through a set of \(k+1\) data points \[ (x_0, y_0), (x_1, y_1), \ldots, (x_k, y_k) \] begin by finding the basis polynomials for each point. The scaled basis polynomial \(L_i(x)\) corresponding to the point \((x_i,y_i)\) is given by \[ L_i(x) = y_i \frac{(x-x_0)}{(x_i-x_0)}\cdot\frac{(x-x_1)}{(x_i-x_1)}\cdots\frac{(x-x_{i-1})}{(x_i-x_{i-1})}\cdot\frac{(x-x_{i+1})}{(x_i-x_{i+1})}\cdots \frac{(x-x_k)}{(x_i-x_k)} \] Each basis polynomial will then have a root at each \(x_j \neq x_i\), and will pass through the point \((x_i,y_i)\). The final interpolating polynomial \(f(x)\) that passes through all data points is the sum of these basis polynomials: \[ f(x) = L_0(x) + L_1(x) + \cdots + L_k(x) \]

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This applet was last updated July 2019.