Random-Facet and Random-Bland require subexponential time even for shortest paths.

The Random-Facet algorithm of Kalai and of Matousek, Sharir and Welzl is an elegant randomized algorithm for solving linear programs and more general LP-type problems. Its expected subexponential time of $2^{\tilde{O}(\sqrt{m})}$, where $m$ is the number of inequalities, makes it the fastest known combinatorial algorithm for solving linear programs. We previously showed that Random-Facet performs an expected number of $2^{\tilde{\Omega}(\sqrt[3]{m})}$ pivoting steps on some LPs with $m$ inequalities that correspond to $m$-action Markov Decision Processes (MDPs). We also showed that Random-Facet-1P, a one permutation variant of Random-Facet, performs an expected number of $2^{\tilde{O}(\sqrt{m})}$ pivoting steps on these examples. Here we show that the same results can be obtained using LPs that correspond to instances of the classical shortest paths problem. This shows that the stochasticity of the MDPs, which is essential for obtaining lower bounds for Random-Edge, is not needed in order to obtain lower bounds for Random-Facet. We also show that our new $2^{\tilde{\Omega}(\sqrt{m})}$ lower bound applies to Random-Bland, a randomized variant of the classical anti-cycling rule suggested by Bland.