  Lack of memory (locality in time) is a major limitation of almost all present
time-dependent density functional approximations. By using semiclassical
dynamics to compute correlation effects within a density-matrix functional
approach, we incorporate memory, including initial-state dependence, as well as
changing occupation numbers, and predict more observables in strong-field
applications.
