Variants of the Randomized Kaczmarz Algorithm and its Applications
Anna Ma
UCSD
Abstract:
Nowadays, data is exploding at a faster rate than computer architectures can
handle. For that reason, mathematical techniques to analyze large-scale data
need be developed. Stochastic iterative algorithms have gained interest due to
their low memory footprint and adaptability for large-scale data. In this talk,
we will present the Randomized Kaczmarz algorithm for solving extremely large
linear systems of the form Ax=y. In the spirit of large-scale data, this talk
will act under the assumption that the entire data matrix A cannot be loaded
into memory in a single instance. We consider different settings including when
a only factorization of A is available, when A is missing information, and a
time-varying model. We will also present applications of these Kaczmarz
variants to problems in data science.