Algorithms and Complexity for some Multivariate Problems

We study multivariate problems like function approximation, numerical integration, global optimization and dispersion. We obtain new results on the information complexity $n(\epsilon ,d)$ of these problems. The information complexity is the amount of information (e.g. the number of function values) that is needed to solve the $d$-dimensional problem up to a prescribed error $\epsilon >0$. We present optimal algorithms for some of these problems. An extended abstract can be found in the section “Introduction and Results”.