We apply proof mining methods to analyse a result of Boikanyo and Moro?anu on the strong convergence Halpern-type proximal point algorithm. As consequence, we obtain quantitative versions this result, providing uniform effective rates asymptotic regularity metastability.

An unsupervised iterative N-dimensional point-set registration algorithm for unlabeled data (i.e., the correspondence between points is unknown) based on linear least squares proposed. The considers all possible point pairings and iteratively aligns two sets until number of pairs does not exceed maximum allowable one-to-one pairings.