Optimization by thermal cycling

Thermal cycling is an heuristic optimization algorithm which consists of cyclically heating and quenching by Metropolis and local search procedures, respectively, where the amplitude slowly decreases. In recent years, it has been successfully applied to two combinatorial optimization tasks, the traveling salesman problem and the search for low-energy states of the Coulomb glass. In these cases, the algorithm is far more efficient than usual simulated annealing. In its original form the algorithm was designed only for the case of discrete variables. Its basic ideas are applicable also to a problem with continuous variables, the search for low-energy states of Lennard-Jones clusters.Comment: Submitted to Proceedings of the Workshop "Complexity, Metastability and Nonextensivity", held in Erice 20-26 July 2004. Latex, 7 pages, 3 figure

Non-ergodic effects in the Coulomb glass: specific heat

We present a numerical method for the investigation of non-ergodic effects in the Coulomb glass. For that, an almost complete set of low-energy many-particle states is obtained by a new algorithm. The dynamics of the sample is mapped to the graph formed by the relevant transitions between these states, that means by transitions with rates larger than the inverse of the duration of the measurement. The formation of isolated clusters in the graph indicates non-ergodicity. We analyze the connectivity of this graph in dependence on temperature, duration of measurement, degree of disorder, and dimensionality, studying how non-ergodicity is reflected in the specific heat.Comment: Submited Phys. Rev.