Probabilistic complexity analysis for linear problems in bounded domains
In the study of information-based complexity let p be the probabilistic complexity for solving a problem on a bounded domain. The p can be interpreted as the minimal number of information operations needed to solve the problem with the given precision requirement. Using tools from the Banach space theory, the author provides two-sided estimates of p with upper and lower bounds differing only by a constant factor, for the approximation of functions of the periodic Sobolev class in the Hilbert space.
scientific article; zbMATH DE number 4193118
scientific article