Speaker
Anna Ma
(University of California, Irvine)
Description
Solving linear systems is a crucial subroutine and challenge in data science and scientific computing. Classical approaches to solving linear systems assume that data is readily available and sufficiently small to be stored in memory. However, in the large-scale data setting, data may be so large that only partitions (e.g., single rows/columns of the matrix/tensor) can be utilized at a time. In this presentation, we discuss our recent progress designing and analyzing tensor-based iterative methods, which in settings that are heavily memory-constrained, when: (i) only row/column slices can be used, (ii) only frontal slices can be used, and (iii) only randomly accessed elements can be used in each iteration.
Author
Anna Ma
(University of California, Irvine)