Georgia Institute of Technology College of Sciences

Ptychographic phase retrieval is an ill-posed nonlinear inverse problem typically formulated as large-scale nonconvex optimization. I will present stochastic multigrid methods, beginning with the known-probe case as a foundation for blind ptychography. The approach uses quadratic surrogate models that majorize the data-misfit objective, together with compatible coarse-grid models that accelerate surrogate minimization. We extend this construction to blind ptychography, where the object and probe must be recovered jointly. Geometric-mean updates coordinate changes to the object and probe within each scan, while intensity-weighted minibatch synthesis combines the resulting local updates across scan positions. I will discuss the mathematical analysis and present results using synthetic data and experimental X-ray measurements.