Randomized Complexity of Parametric Integration and the Role of Adaption II. Sobolev Spaces

TL;DR

This paper investigates the randomized computational complexity of parametric integration for functions in Sobolev spaces, extending previous results and introducing a new stochastic discretization method to handle cases without the embedding condition.

Contribution

It extends prior complexity results to Sobolev spaces without the embedding condition and develops a novel stochastic discretization technique for this setting.

Findings

Extended complexity bounds for Sobolev space integrands.

Introduced a stochastic discretization method for non-embedding cases.

Analyzed the role of adaptivity in randomized parametric integration.

Abstract

We study the complexity of randomized computation of integrals depending on a parameter, with integrands from Sobolev spaces. That is, for $r,d_1,d_2\in{\mathbb N}$, $1\le p,q\le \infty$, $D_1= [0,1]^{d_1}$, and $D_2= [0,1]^{d_2}$ we are given $f\in W_p^r(D_1\times D_2)$ and we seek to approximate $$ Sf=\int_{D_2}f(s,t)dt\quad (s\in D_1), $$ with error measured in the $L_q(D_1)$-norm. Our results extend previous work of Heinrich and Sindambiwe (J.\ Complexity, 15 (1999), 317--341) for $p=q=\infty$ and Wiegand (Shaker Verlag, 2006) for $1\le p=q<\infty$. Wiegand's analysis was carried out under the assumption that $W_p^r(D_1\times D_2)$ is continuously embedded in $C(D_1\times D_2)$ (embedding condition). We also study the case that the embedding condition does not hold. For this purpose a new ingredient is developed -- a stochastic discretization technique. The paper is based on Part I, where vector valued mean computation -- the finite-dimensional counterpart of parametric integration -- was studied. In Part I a basic problem of Information-Based Complexity on the power of adaption for linear problems in the randomized setting was solved. Here a further aspect of this problem is settled.