Average case tractability of non-homogeneous tensor product problems
We study d-variate approximation problems in the average case setting with respect to a zero-mean Gaussian measure v(d). Our interest is focused on measures having the structure of a non-homogeneous tensor product, where the covariance kernel of v(d) is a product of univariate kernels: K-d(s, t) = (k=1)Pi(d) R-k(s(k), t(k)) for s, t epsilon vertical bar 0.1 vertical bar(d). We consider the normalized average error of algorithms that use finitely many evaluations of arbitrary linear functionals. The information complexity is defined as the minimal number n(avg)(epsilon, d) of such evaluations for error in the d-variate case to be at most epsilon. The growth of n(avg)(epsilon, d) as a function of epsilon(-1) d depends on the eigenvalues of the covariance operator of v(d) and determines whether a problem is tractable or not. Four types of tractability are studied and for each of them we find the necessary and sufficient conditions in terms of the eigenvalues of the integral operator with kernel R-k. We illustrate our results by considering approximation problems related to the product of Korobov kernels R-k. Each R-k is characterized by a weight g(k) and a smoothness r(k). We assume that weights are non-increasing and smoothness parameters are nondecreasing. Furthermore they may be related; for instance g(k) = g(r(k)) for some non-increasing function g. In particular, we show that the approximation problem is strongly polynomially tractable, i.e., n(avg)(epsilon, d)   infinity) ln 1/gk/ln k >1 For other types of tractability we also show necessary and sufficient conditions in terms of the sequences g(k) and r(k). (c) 2012 Elsevier Inc. All rights reserved.
tractability multivariate problems