@article{BaakeBaakeBovieretal.2005,
  author    = {Baake, Ellen and Baake, Michael and Bovier, Anton and Klein, Markus},
  title     = {An asymptotic maximum principle for essentially linear evolution models},
  issn      = {0303-6812},
  year      = {2005},
  abstract  = {Recent work on mutation-selection models has revealed that, under specific assumptions on the fitness function and the mutation rates, asymptotic estimates for the leading eigenvalue of the mutation-reproduction matrix may be obtained through a low-dimensional maximum principle in the limit N --> infinity (where N, or N-d with d greater than or equal to 1, is proportional to the number of types). In order to extend this variational principle to a larger class of models, we consider here a family of reversible matrices of asymptotic dimension N-d and identify conditions under which the high-dimensional Rayleigh-Ritz variational problem may be reduced to a low-dimensional one that yields the leading eigenvalue up to an error term of order 1/N. For a large class of mutation-selection models, this implies estimates for the mean fitness, as well as a concentration result for the ancestral distribution of types},
  language  = {en}
}