Speaker: Robert Krauthgamer
Affiliation: Weizmann Institute of Science
Title: On Solving Linear Systems in Sublinear Time
I will discuss sublinear algorithms that solve linear systems locally. In
the classical version of this problem, the input is a matrix S and a vector
b in the range of S, and the goal is to output a vector x satisfying Sx=b.
We focus on computing (approximating) one coordinate of x, which potentially
allows for sublinear algorithms. Our results show that there is a
qualitative gap between symmetric diagonally dominant (SDD) and the more
general class of positive semidefinite (PSD) matrices. For SDD matrices, we
develop an algorithm that runs in polylogarithmic time, provided that S is
sparse and has a small condition number (e.g., Laplacian of an expander
graph). In contrast, for certain PSD matrices with analgous assumptions, the
running time must be at least polynomial.
Joint work with Alexandr Andoni and Yosef Pogrow.