  We consider blind ptychography, an imaging technique which aims to
reconstruct an object of interest from a set of its diffraction patterns, each
obtained by a local illumination. As the distribution of the light within the
illuminated region, called the window, is unknown, it also has to be estimated
as well. For the recovery, we consider gradient and stochastic gradient descent
methods for the minimization of amplitude-base squared loss. In particular,
this includes extended Ptychographic Iterative Engine as a special case of
stochastic gradient descent. We show that all methods converge to a critical
point at a sublinear rate with a proper choice of step sizes. We also discuss
possibilities for larger step sizes.
