We consider the 1D Expected Improvement optimization based on Gaussian
processes having spectral densities converging to zero faster than
exponentially. We give examples of problems where the optimization trajectory
is not dense in the design space. In particular, we prove that for Gaussian
kernels there exist smooth objective functions for which the optimization does
not converge on the optimum.