  The Propagation-Separation approach is an iterative procedure for pointwise
estimation of local constant and local polynomial functions. The estimator is
defined as a weighted mean of the observations with data-driven weights. Within
homogeneous regions it ensures a similar behavior as non-adaptive smoothing
(propagation), while avoiding smoothing among distinct regions (separation). In
order to enable a proof of stability of estimates, the authors of the original
study introduced an additional memory step aggregating the estimators of the
successive iteration steps. Here, we study theoretical properties of the
simplified algorithm, where the memory step is omitted. In particular, we
introduce a new strategy for the choice of the adaptation parameter yielding
propagation and stability for local constant functions with sharp
discontinuities.
