Polynomial Mixing Times of Simulated Tempering for Mixture Targets by Conductance Decomposition

We study the theoretical complexity of simulated tempering for sampling from mixtures of log-concave components differing only by location shifts. The main result establishes the first polynomial-time guarantee for simulated tempering combined with the Metropolis-adjusted Langevin algorithm (MALA) with respect to the problem dimension $d$, maximum mode displacement $D$, and logarithmic accuracy $\log {\epsilon }^{-1}$. The proof builds on a general state decomposition theorem for $s$-conductance, applied to an auxiliary Markov chain constructed on an augmented space. We also obtain an improved complexity estimate for simulated tempering combined with random-walk Metropolis. Our bounds assume an inverse-temperature ladder with smallest value ${\beta }_1=O(D^{-2})$ and spacing ${\beta }_{i+1}\mathrm{/}{\beta }_i=1+O(d^{-1\mathrm{/}2})$, both of which are shown to be asymptotically optimal up to logarithmic factors.