  The convergence and optimality theory of adaptive Galerkin methods is almost
exclusively based on the D\"orfler marking. This entails a fixed parameter and
leads to a contraction constant bounded below away from zero. For spectral
Galerkin methods this is a severe limitation which affects performance. We
present a dynamic marking strategy that allows for a super-linear relation
between consecutive discretization errors, and show exponential convergence
with linear computational complexity whenever the solution belongs to a Gevrey
approximation class.
