Ptychography is an increasingly popular imaging technique that has found application in a wide variety of experimental contexts, for imaging with X-rays, visible light, or electrons, in different experimental geometries---far-field or near-field, transmission or Bragg---and even with overlapping angles instead of positions. As the experimental model increases in complexity, the number and type of variables we could optimize for increases in scope, and it is more difficult and tedious to formulate an inversion procedure. 

We address this challenge by using modern automatic differentiation (AD) methods to design inversion procedures that do not require explicit calculations of closed-form gradient expressions. We show that we can use first-order as well as second-order AD methods to enable fast, memory-efficient, and robust solutions to ptychographic inversion problems. Remarkably, our second-order optimization approach incurs a computational cost that is often lower than that required by state-of-the-art first order ptychography algorithms. The algorithms we develop are general in scope and easy to apply for a variety of optimization problems beyond just the ptychography setting.

Please use this link to attend the virtual seminar.

https://argonne.zoomgov.com/j/1615894921