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Kolmogorov famously proved that multivariate continuous functions can be represented as a superposition of a small number of univariate continuous functions, $$ f(x_1,dots,x_n) = sum_{q=0}^{2n+1} chi^q left( sum_{p=1}^n psi^{pq}(x_p) right).$$ Fridman cite{fridman} posed the best smoothness bound for the functions $psi^{pq}$, that such functions can be constructed to be Lipschitz continuous with constant 1. Previous algorithms to describe these inner functions have only been Holder continuous, such as those proposed by Koppen and Braun and Griebel. This is problematic, as pointed out by Griebel, in that non-smooth functions have very high storage/evaluation complexity, and this makes Kolmogorovs representation (KR) impractical using the standard definition of the inner functions. To date, no one has presented a method to compute a Lipschitz continuous inner function. In this paper, we revisit Kolmogorovs theorem along with Fridmans result. We examine a simple Lipschitz function which appear to satisfy the necessary criteria for Kolmogorovs representation, but fails in the limit. We then present a full solution to the problem, including an algorithm that computes such a Lipschitz function.
We present a new fast algorithm for computing the Boys function using nonlinear approximation of the integrand via exponentials. The resulting algorithms evaluate the Boys function with real and complex valued arguments and are competitive with previously developed algorithms for the same purpose.
Parametric sensitivity analysis is a critical component in the study of mathematical models of physical systems. Due to its simplicity, finite difference methods are used extensively for this analysis in the study of stochastically modeled reaction n
Developments of nonlocal operators for modeling processes that traditionally have been described by local differential operators have been increasingly active during the last few years. One example is peridynamics for brittle materials and another is
We investigate the problem of approximating the matrix function $f(A)$ by $r(A)$, with $f$ a Markov function, $r$ a rational interpolant of $f$, and $A$ a symmetric Toeplitz matrix. In a first step, we obtain a new upper bound for the relative interp
We present a fast method for evaluating expressions of the form $$ u_j = sum_{i = 1,i ot = j}^n frac{alpha_i}{x_i - x_j}, quad text{for} quad j = 1,ldots,n, $$ where $alpha_i$ are real numbers, and $x_i$ are points in a compact interval of $mathbb{R