  This paper introduces a novel algorithmic solution for the approximation of a
given multivariate function by a nomographic function that is composed of a
one-dimensional continuous and monotone outer function and a sum of univariate
continuous inner functions. We show that a suitable approximation can be
obtained by solving a cone-constrained Rayleigh-Quotient optimization problem.
The proposed approach is based on a combination of a dimensionwise function
decomposition known as Analysis of Variance (ANOVA) and optimization over a
class of monotone polynomials. An example is given to show that the proposed
algorithm can be applied to solve problems in distributed function computation
over multiple-access channels.
