Optimal stack filtering and classical Bayes decision

Optimal stack filtering under the mean absolute error (MAE) criterion is studied. It is first shown that this problem is equivalent to the classical a priori Bayes minimum-cost decision. Generally, a linear program (LP) with O(b2^b) variables and constraints (b is the window width) is required for finding the best filter. Instead, the authors develop a suboptimal routine which renders the use of the LP obsolete, but yields reasonably good filters. Sufficient conditions under which the proposed routine results in optimal solutions are provided and shown to hold in most practical cases. Several design examples are given