Suppose I have a stochastic matrix $M$ (with thousands or millions of stochastic column vectors), which I split into two matrices: $D$ containing only the diagonal entries of $M$, and $R$ containing the remaining entries of $M$. What would be a fast way to compute $D(IR)^{1}$ (or a good approximation)? Instead of low computational complexity, I'm looking for fast practical performance. Thanks. Michelle
