Markus Weimar

Notes on ( s , t ) -weak tractability

In the last 20 years a whole hierarchy of notions of tractability was proposed and analyzed by several authors. These notions are used to classify the computational hardness of continuous numerical problems S = ( S d ) d ? N in terms of the behavior of their information complexity n ( e , S d ) as a function of the accuracy e and the dimension d . By now a lot of effort was spent on either proving quantitative positive results (such as, e.g., the concrete dependence on e and d within the well-established framework of polynomial tractability), or on qualitative negative results (which, e.g., state that a given problem suffers from the so-called curse of dimensionality). Although several weaker types of tractability were introduced recently, the theory of information-based complexity still lacks a notion which allows to quantify the exact (sub-/super-) exponential dependence of n ( e , S d ) on both parameters e and d . In this paper we present the notion of ( s , t ) -weak tractability which attempts to fill this gap. Within this new framework the parameters s and t are used to quantitatively refine the huge class of polynomially intractable problems. For linear, compact operators between Hilbert spaces we provide characterizations of ( s , t ) -weak tractability w.r.t.?the worst case setting in terms of singular values. In addition, our new notion is illustrated by classical examples which recently attracted some attention. In detail, we study approximation problems between periodic Sobolev spaces and integration problems for classes of smooth functions.

Rank-1 lattice rules for multivariate integration in spaces of permutation-invariant functions

We study multivariate integration of functions that are invariant under permutations (of subsets) of their arguments. We find an upper bound for the nth minimal worst case error and show that under certain conditions, it can be bounded independent of the number of dimensions. In particular, we study the application of unshifted and randomly shifted rank-1 lattice rules in such a problem setting. We derive conditions under which multivariate integration is polynomially or strongly polynomially tractable with the Monte Carlo rate of convergence 𝓞(n−1/2)

Adaptive Wavelet BEM for Boundary Integral Equations: Theory and Numerical Experiments

\mathcal {O}(n^{-1/2})

Construction of Quasi-Monte Carlo Rules for Multivariate Integration in Spaces of Permutation-Invariant Functions

. Furthermore, we prove that those tractability results can be achieved with shifted lattice rules and that the shifts are indeed necessary. Finally, we show the existence of rank-1 lattice rules whose worst case error on the permutation- and shift-invariant spaces converge with (almost) optimal rate. That is, we derive error bounds of the form 𝓞(n−λ/2)

Probabilistic Star Discrepancy Bounds for Double Infinite Random Matrices

\mathcal {O}(n^{-\lambda /2})

Breaking the curse of dimensionality

for all 1≤λ<2α, where α denotes the smoothness of the spaces.

Tractability results for weighted Banach spaces of smooth functions

ABSTRACT We are concerned with the numerical treatment of boundary integral equations by the adaptive wavelet boundary element method. In particular, we consider the second kind Fredholm integral equation for the double layer potential operator on patchwise smooth manifolds contained in ℝ3. The corresponding operator equations are treated by adaptive implementations that are in complete accordance with the underlying theory. The numerical experiments demonstrate that adaptive methods really pay off in this setting. The observed convergence rates fit together very well with the theoretical predictions based on the Besov regularity of the exact solution.

Besov regularity for operator equations on patchwise smooth manifolds

We study multivariate integration of functions that are invariant under the permutation (of a subset) of their arguments. Recently, in Nuyens et al. (Adv Comput Math 42(1):55–84, 2016), the authors derived an upper estimate for the nth minimal worst case error for such problems and showed that under certain conditions this upper bound only weakly depends on the dimension. We extend these results by proposing two (semi-) explicit construction schemes. We develop a component-by-component algorithm to find the generating vector for a shifted rank-1 lattice rule that obtains a rate of convergence arbitrarily close to

Full length article: The complexity of linear tensor product problems in (anti)symmetric Hilbert spaces

\mathcal {O}(n^{-\alpha })