Constructive Approximation

, Volume 42, Issue 2, pp 231–264 | Cite as

Entropy and Sampling Numbers of Classes of Ridge Functions



We study the properties of ridge functions \(f(x)=g(a\cdot x)\) in high dimensions \(d\) from the viewpoint of approximation theory. The function classes considered consist of ridge functions such that the profile \(g\) is a member of a univariate Lipschitz class with smoothness \(\alpha >0\) (including infinite smoothness) and the ridge direction \(a\) has \(p\)-norm \(\Vert a\Vert _p\le 1\). First, we investigate entropy numbers in order to quantify the compactness of these ridge function classes in \(L_{\infty }\). We show that they are essentially as compact as the class of univariate Lipschitz functions. Second, we examine sampling numbers and consider two extreme cases. In the case \(p=2\), sampling ridge functions on the Euclidean unit ball suffers from the curse of dimensionality. Moreover, it is as difficult as sampling general multivariate Lipschitz functions, which is in sharp contrast to the result on entropy numbers. When we additionally assume that all feasible profiles have a first derivative uniformly bounded away from zero at the origin, the complexity of sampling ridge functions reduces drastically to the complexity of sampling univariate Lipschitz functions. In between, the sampling problem’s degree of difficulty varies, depending on the values of \(\alpha \) and \(p\). Surprisingly, we see almost the entire hierarchy of tractability levels as introduced in the recent monographs by Novak and Woźniakowski.


Ridge functions Sampling numbers Entropy numbers  Rate of convergence Information-based complexity Curse of dimensionality 

Mathematics Subject Classification

41A10 41A25 41A50 41A63 46E35 65D05 65D15 



The authors would like to thank Aicke Hinrichs, Erich Novak, and Mario Ullrich for pointing out relations to the paper [19], as well as Sjoerd Dirksen, Thomas Kühn, and Winfried Sickel for useful comments and discussions. The last author acknowledges the support by the DFG Research Center Matheon “Mathematics for key technologies” in Berlin. The last author was supported by the ERC CZ grant LL1203 of the Czech Ministry of Education.


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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Hausdorff-Center for MathematicsBonnGermany
  2. 2.Department of Mathematical AnalysisCharles UniversityPrague 8Czech Republic

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