We prove that any Markov chain that performs local, reversible updates on
randomly chosen vertices of a bounded-degree graph necessarily has mixing time
at least $\Omega(n\log n)$, where $n$ is the number of vertices. Our bound
applies to the so-called ``Glauber dynamics'' that has been used extensively in
algorithms for the Ising model, independent sets, graph colorings and other
structures in computer science and statistical physics, and demonstrates that
many of these algorithms are optimal up to constant factors within their class.
Previously, no superlinear lower bound was known for this class of algorithms.
Though widely conjectured, such a bound had been proved previously only in very
restricted circumstances, such as for the empty graph and the path. We also
show that the assumption of bounded degree is necessary by giving a family of
dynamics on graphs of unbounded degree with mixing time O(n).