# Block LMS with overlapping blocks

In the Block LMS algorithm, the input is partitioned into nonoverlapping blocks of size $L$ and the filter coefficients are updated once every $L$ samples. Would convergence improve if we used more frequent filter coefficient updates by using overlapping blocks? Having more "descent steps" per unit time would seem to imply faster convergence, but the lack of mention of this in any literature suggests otherwise.