@article{Mann:Klemm:11,
  author =	{Martin Mann and Konstantin Klemm},
  title =	{Efficient exploration of discrete energy landscapes},
  journal =	{Phys. Rev. E},
  issn = 	{1539-3755},
  volume = 	{83},
  number = 	{1},
  pages =	{online},
  year =	{2011},
  month =       {January},
  doi = 	{10.1103/PhysRevE.83.011113},
  arxiv =	{0910.2559},
  user =	{mmann},
  abstract =	{Many physical and chemical processes, such as folding of biopolymers, are best
described as dynamics on large combinatorial energy landscapes. A concise
approximate description of the dynamics is obtained by partitioning the
micro-states of the landscape into macro-states. Since most landscapes of
interest are not tractable analytically, the probabilities of transitions
between macro-states need to be extracted numerically from the microscopic ones,
typically by full enumeration of the state space or approximations using the
Arrhenius law. Here we propose to approximate transition probabilities by a
Markov chain Monte-Carlo method. For landscapes of the number partitioning
problem and an RNA switch molecule we show that the method allows for accurate
probability estimates with significantly reduced computational cost.}
}