ε-superposition and truncation dimensions in average and probabilistic settings for ∞-variate linear problems

The paper deals with linear problems defined on γ -weighted Hilbert spaces of functions with infinitely many variables. The spaces are endowed with zero-mean Gaussian measures which allows to define and study e -truncation and e -superposition dimensions in the average case and probabilistic settings. Roughly speaking, these e -dimensions quantify the smallest number k = k ( e ) of variables that allow to approximate the ∞ -variate functions by special ones that depend on at most k -variables with the average error bounded by e . In the probabilistic setting, given δ ∈ ( 0 , 1 ) , we want the error ≤ e with probability ≥ 1 − δ . We show that the e -dimensions are surprisingly small which, for anchored spaces, leads to very efficient algorithms, including the Multivariate Decomposition Methods.