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Gradient-type algorithms commonly employ a scalar step-size, i.e., each entry of the regression vector is multiplied by the same value before updating the coefficients. More flexibility, however, is obtained when this step-size is of matrix size. It allows not only to individually scaling the entries of the regression vector but rotations and decorrelations are possible as well due to the choice of...