paper-with-me

Papers

Nonlinear tensor product approximation of functions

2014-09-04 · D. Bazarkhanov, V. Temlyakov

We are interested in approximation of a multivariate function $f(x_1,\dots,x_d)$ by linear combinations of products $u^1(x_1)\cdots u^d(x_d)$ of univariate functions $u^i(x_i)$, $i=1,\dots,d$. In the case $d=2$ it is a classical problem of bilinear approximation. In the case of approximation in the $L_2$ space the bilinear approximation problem is closely related to the problem of singular value decomposition (also called Schmidt expansion) of the corresponding integral operator with the kernel $f(x_1,x_2)$. There are known results on the rate of decay of errors of best bilinear approximation in $L_p$ under different smoothness assumptions on $f$. The problem of multilinear approximation (nonlinear tensor product approximation) in the case $d\ge 3$ is more difficult and much less studied than the bilinear approximation problem. We will present results on best multilinear approximation in $L_p$ under mixed smoothness assumption on $f$.

📄 PDF Abstract BibTeX arXiv:1409.1403

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations

2026-05-28 · Qihong Yang, Yangtao Deng, Qiaolin He, Shiquan Zhang arxiv

This paper presents the Tensor Product Network (TPNet), a novel neural architecture for efficient and accurate function approximation and PDE solving. The core of the proposal involves constructing the solution explicitl…

Computational Efficiency

Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part I

2020-06-30 · Mazen Ali, Anthony Nouy

We study the approximation of functions by tensor networks (TNs). We show that Lebesgue $L^p$-spaces in one dimension can be identified with tensor product spaces of arbitrary order through tensorization. We use this ten…

Tensor Networks

Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part II

2020-06-30 · Mazen Ali, Anthony Nouy

We study the approximation by tensor networks (TNs) of functions from classical smoothness classes. The considered approximation tool combines a tensorization of functions in $L^p([0,1))$, which allows to identify a univ…

Tensor Networks

Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties

2013-10-18 · Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens

We present a general framework to learn functions in tensor product reproducing kernel Hilbert spaces (TP-RKHSs). The methodology is based on a novel representer theorem suitable for existing as well as new spectral pena…

Transfer Learning

When big data actually are low-rank, or entrywise approximation of certain function-generated matrices

2024-07-03 · Stanislav Budzinskiy

The article concerns low-rank approximation of matrices generated by sampling a smooth function of two $m$-dimensional variables. We identify several misconceptions surrounding a claim that, for a specific class of analy…

Misconceptions