  A class of restarted randomized surrounding methods are presented to
accelerate the surrounding algorithms by restarted techniques for solving the
linear equations. Theoretical analysis prove that the proposed method converges
under the randomized row selection rule and the expectation convergence rate is
also addressed. Numerical experiments further demonstrate that the proposed
algorithms are efficient and outperform the existing method for over-determined
and under-determined linear equation, as well as in the application of image
processing.
