We present a model for the optimal design of an online auction/store by a
seller. The framework we use is a stochastic optimal control problem. In our
setting, the seller wishes to maximize her average wealth level, where she can
control her price per unit via her reputation level. The corresponding
Hamilton-Jacobi-Bellmann equation is analyzed for an introductory case. We then
turn to an empirically justified model, and present introductory analysis. In
both cases, {\em pulsing} advertising strategies are recovered for resource
allocation.
We propose and study quantitative measures of smoothness which are adapted to
anisotropic features such as edges in images or shocks in PDE's. These
quantities govern the rate of approximation by adaptive finite elements, when
no constraint is imposed on the aspect ratio of the triangles, the simplest
examples of such quantities are based on the determinant of the hessian of the
function to be approximated. Since they are not semi-norms, these quantities
cannot be used to define linear function spaces.