SVD-based approximations of bivariate functions

A method to approximate functions of two variables is presented; it is suitable for hardware implementations based on digital or mixed signal architectures. Such a method is based on the properties of the singular value decomposition (SVD) of a matrix that stores the samples of the function to be approximated. The considered SVD-based approximations are expressed as products of functions of a single variable and are built up as combinations of proper sets of piecewise-linear or polynomial basis functions. In the proposed examples, the accuracy of the SVD-based approximations is compared with that obtained by resorting to two well-assessed methods.