Cubic spline system program. Suppose we have empirical data {(x1, y1), (x2, y2), …, (xn, yn)} that was obtained from some experiment. We have strong reason to believe y is related to x by some smooth function, but we do not know what that function is. We would like to find a function that can be used to approximate values of y for given values of x between the given data points. There are many ways to approach this problem, but the three most common are least squares, Lagrange interpolation, and cubic splines. Each of these methods has advantages and limitations compared to the other two. Includes PDF documentation. |