On the Loewner Framework, the Kolmogorov Superposition Theorem, and the Curse of Dimensionality

SIAM Review, Volume 67, Issue 4, Page 737-770, December 2025.
Abstract.The Loewner framework is an interpolatory approach for the approximation of linear and nonlinear systems. The purpose here is to extend this framework to linear parametric systems with an arbitrary number [math] of parameters. To achieve this, a new generalized multivariate rational function realization is proposed. We then introduce the [math]-dimensional multivariate Loewner matrices and show that they can be computed by solving a set of coupled Sylvester equations. The null space of these Loewner matrices allows the construction of multivariate rational functions in barycentric form. The principal result of this work is to show how the null space of [math]-dimensional Loewner matrices can be computed using a sequence of one-dimensional Loewner matrices. Thus, a decoupling of the variables is achieved, which leads to a drastic reduction of the computational burden. Equally importantly, this burden is alleviated by avoiding the explicit construction of large-scale [math]-dimensional Loewner matrices of size [math]. The proposed methodology achieves the decoupling of variables, leading (i) to a reduction in complexity from [math] to below [math] when [math] and (ii) to memory storage bounded by the largest variable dimension rather than their product, thus taming the curse of dimensionality and making the solution scalable to very large data sets. This decoupling of the variables leads to a result similar to the Kolmogorov superposition theorem for rational functions. Thus, making use of barycentric representations, every multivariate rational function can be computed using the composition and superposition of single-variable functions. Finally, we suggest two algorithms (one direct and one iterative) to construct, directly from data, multivariate (or parametric) realizations ensuring (approximate) interpolation. Numerical examples highlight the effectiveness and scalability of the method.