We study QPT (quasi-polynomial tractability) in the worst case setting for
linear tensor product problems defined over Hilbert spaces. We assume that the
domain space is a reproducing kernel Hilbert space so that function values are
well defined. We prove QPT for algorithms that use only function values under
the three assumptions:
1) the minimal errors for the univariate case decay polynomially fast to
zero,
2) the largest singular value for the univariate case is simple and
3) the eigenfunction corresponding to the largest singular value is a
multiple of the function value at some point.
The first two assumptions are necessary for QPT. The third assumption is
necessary for QPT for some Hilbert spaces
