that falls over the same interval of energy values, normalized by the total number of samples. For ΔT = 0 the overlap should approach 1. Another way to interpret this overlap is to say that system configurations sampled at temperature T1 are likely to appear during a simulation at T2. Because the Markov chain should have no memory of its past, we can create a new update for the system composed of the two systems at T1 and T2. At a given Monte Carlo step we can update the global system by swapping the configuration of the two systems, or alternatively trading the two temperatures. The update is accepted according to the Metropolis–Hastings criterion with probability and otherwise the update is rejected. The detailed balance condition has to be satisfied by ensuring that the reverse update has to be equally likely, all else being equal. This can be ensured by appropriately choosing regular Monte Carlo updates or parallel tempering updates with probabilities that are independent of the configurations of the two systems or of the Monte Carlo step. This update can be generalized to more than two systems. By a careful choice of temperatures and number of systems one can achieve an improvement in the mixing properties of a set of Monte Carlo simulations that exceeds the extra computational cost of running parallel simulations. Other considerations to be made: increasing the number of different temperatures can have a detrimental effect, as one can think of the 'lateral' movement of a given system across temperatures as a diffusion process. Set up is important as there must be a practical histogram overlap to achieve a reasonable probability of lateral moves. The parallel tempering method can be used as a super simulated annealing that does not need restart, since a system at high temperature can feed new local optimizers to a system at low temperature, allowing tunneling between metastable states and improving convergence to a global optimum. Implementations See also Bennett acceptance ratio References Markov chain Monte Carlo Molecular dynamics Heuristics Statistical mechanics Stochastic optimization