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.

翻译：理论分析证明,拟议的方法符合随机行选择规则和预期汇合率也得到了处理。 数字实验进一步证明,拟议的算法效率高,超过了现有定额过高和定额不足的线性方程方法,也超过了图象处理的应用。