arXiv subjects

Jan Schneider

Publications and source records attributed to Jan Schneider.

22 records · Page 2Linked to original sources

arXiv2017

Convergence of fuzzy random walks to a standard Brownian motion

In this note - starting from $d$-dimensional (with $d>1$) fuzzy vectors - we prove Donsker's classical invariance principle. We consider a fuzzy random walk ${S^*_n}=X^*_1+\cdots+X^*_n,$ where $\{X^*_i\}_1^{\infty}$ is a sequence of mutually independent and identically distributed $d$-dimensional fuzzy random variables whose $α$-cuts are assumed to be compact and convex. Our reasoning and technique are based on the well known conjugacy correspondence between convex sets and support functions, which allows for the association of an appropriately normalized and interpolated time-continuous fuzzy random process with a real valued random process in the space of support functions. We show that each member of the associated family of dual sequences tends in distribution to a standard Brownian motion.

math.PR↗

arXiv2017

Quantized-CP Approximation and Sparse Tensor Interpolation of Function Generated Data

In this article we consider the iterative schemes to compute the canonical (CP) approximation of quantized data generated by a function discretized on a large uniform grid in an interval on the real line. This paper continues the research on the QTT method [16] developed for the tensor train (TT) approximation of the quantized images of function related data. In the QTT approach the target vector of length $2^{L}$ is reshaped to a $L^{th}$ order tensor with two entries in each mode (Quantized representation) and then approximated by the QTT tenor including $2r^2 L$ parameters, where $r$ is the maximal TT rank. In what follows, we consider the Alternating Least-Squares (ALS) iterative scheme to compute the rank-$r$ CP approximation of the quantized vectors, which requires only $2 r L\ll 2^L$ parameters for storage. In the earlier papers [17] such a representation was called Q$_{Can}$ format, while in this paper we abbreviate it as the QCP representation. We test the ALS algorithm to calculate the QCP approximation on various functions, and in all cases we observed the exponential error decay in the QCP rank. The main idea for recovering a discretized function in the rank-$r$ QCP format using the reduced number the functional samples, calculated only at $O(2rL)$ grid points, is presented. The special version of ALS scheme for solving the arising minimization problem is described. This approach can be viewed as the sparse QCP-interpolation method that allows to recover all $2r L$ representation parameters of the rank-$r$ QCP tensor. Numerical examples show the efficiency of the QCP-ALS type iteration and indicate the exponential convergence rate in $r$.

math.NA↗

arXiv2016

Arithmetic of fuzzy numbers and intervals -- a new perspective with examples

This article is meant to give a lucid and widely accessible, self-contained account of a novel way of performing arithmetic operations on fuzzy intervals. Based on two formulae of generalized inversion (the first in close analogy to the inversion of cumulative distribution functions in probability and statistics, and subsequent re-inversion which in this form seems to be new in the literature) the technique could prove to be of some importance for the practical handling of fuzzy intervals whose characterizing functions are discontinuous and/or not strictly monotonic, e.g. piecewise constant. Notwithstanding its innovative nature, the article is written in the style of an introductory textbook featuring a multitude of illustrated examples.

math.GM↗

arXiv2015

Equivalence of anchored and ANOVA spaces via interpolation

We consider weighted anchored and ANOVA spaces of functions with first order mixed derivatives bounded in $L_p$. Recently, Hefter, Ritter and Wasilkowski established conditions on the weights in the cases $p=1$ and $p=\infty$ which ensure equivalence of the corresponding norms uniformly in the dimension or only polynomially dependent on the dimension. We extend these results to the whole range of $p\in [1,\infty]$. It is shown how this can be achieved via interpolation.

math.NA↗