Polynomial-time algorithms for multivariate linear problems with finite-order weights: Average case setting

We study multivariate linear problems in the average case setting with respect to a zero-mean Gaussian measure whose covariance kernel has a finite-order weights structure. This means that the measure is concentrated on a Banach space of d-variate functions that are sums of functions of at most q ^* variables and the influence of each such term depends on a given weight. Here q ^* is fixed whereas d varies and can be arbitrarily large. For arbitrary finite-order weights, based on Smolyak's algorithm, we construct polynomial-time algorithms that use standard information. That is, algorithms that solve the d-variate problem to within ε using of order -pd q function values modulo a power of ln∈ε ^-1. Here p is the exponent which measures the difficulty of the univariate (d=1) problem, and the power of ln∈ε ^-1 is independent of d. We also present a necessary and sufficient condition on finite-order weights for which we obtain strongly polynomial-time algorithms, i.e., when the number of function values is independent of d and polynomial in ε ^-1. The exponent of ε ^-1 may be, however, larger than p. We illustrate the results by two multivariate problems: integration and function approximation. For the univariate case we assume the r-folded Wiener measure. Then p=1/(r+1) for integration and p=1/(r+ 1 2) for approximation.