Speaker
Eric de Sturler
(Virginia Tech)
Description
Big data applications are becoming ever more prominent, and in many applications we need to solve very large linear or nonlinear inverse problems while handling only a relatively small amount of data at a time. Moreover, we are interested in distributed, possibly asynchronous, algorithms that solve large problems while only exchanging limited information. We need algorithms that combine approximate (partial) solutions with incoming data or data read from secondary memory to incrementally further improve the solution. We will discuss several algorithmic variations and their convergence.
Authors
Eric de Sturler
(Virginia Tech)
Malena Sabate Landman
(University of Bath)
Priyanka Sinha
(Virginia Tech)