TY - JOUR
AB - 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.
AU - Baake, Ellen
AU - Baake, Michael
AU - Bovier, Anton
AU - Klein, Markus
ID - 1604953
IS - 1
JF - JOURNAL OF MATHEMATICAL BIOLOGY
KW - ancestral distribution
KW - models
KW - mutation-selection
KW - lumping
KW - asymptotics of leading eigenvalue
KW - reversibility
SN - 0303-6812
TI - An asymptotic maximum principle for essentially linear evolution models
VL - 50
ER -