On the power of adaption and randomization

Abstract

We present bounds on the maximal gain of adaptive and randomized algorithms over non-adaptive, deterministic ones for approximating linear operators on convex sets. If the sets are additionally symmetric, then our results are optimal. For non-symmetric sets, we unify some notions of $n$-widths and s-numbers, and show their connection to minimal errors. We also discuss extensions to non-linear widths and approximation based on function values, and conclude with a list of open problems.