Weak Poincaré Inequalities and Mixing from Non-Worst-Case Initializations

A Markov chain can sample effectively from a random or data-informed start even when worst-case initialization mixes slowly. This talk uses weak Poincaré inequalities to analyse that regime, with applications to simulated annealing for spherical spin glasses and to mixtures of log-concave distributions.