  In this paper we present a mixed EIM-SVD tensor decomposition for bivariate
functions. This method is composed, as its name suggests, of two main steps.
The first one, provides an approximate representation of a function $f$ in
separate form by the use of a Tensor Empirical Interpolation Method (TEIM). The
second phase consists in applying the Singular Value Decomposition (SVD) with
low-rank truncation to the separate form of $f$ resulting from the first phase.
Error estimates of the developed TEIM as well as truncated SVD decomposition
are derived. Numerical experiments confirm that the decomposition techniques
are efficient in terms of stability and accuracy.
