  The randomized row method is a popular representative of the iterative
algorithm because of its efficiency in solving the overdetermined and
consistent systems of linear equations. In this paper, we present an extended
randomized multiple row method to solve a given overdetermined and inconsistent
linear system and analyze its computational complexities at each iteration. We
prove that the proposed method can linearly converge in the mean square to the
least-squares solution with a minimum Euclidean norm. Several numerical studies
are presented to corroborate our theoretical findings. The real-world
applications, such as image reconstruction and large noisy data fitting in
computer-aided geometric design, are also presented for illustration purposes.
