Regularization is a popular method for interpolating sparse data, as well as smoothing data obtained from noisy measurements. Simply put, regularization looks for an interpolating or approximating function which is both close to the data and also &quot;smooth&quot; in some sense. Formally, this function is obtained by minimizing an error functional which is the sum of two terms, one measuring the distance from the data, the other measuring the smoothness of the function