  We combine two iterative algorithms for solving large-scale systems of linear
inequalities, the relaxation method of Agmon, Motzkin et al. and the randomized
Kaczmarz method. In doing so, we obtain a family of algorithms that generalize
and extend both techniques. We prove several convergence results, and our
computational experiments show our algorithms often outperform the original
methods.
