Course: STAT 31190
Title: Fast Algorithms
Instructor(s): Jeremy Hoskins
Teaching Assistant(s): TBD
Class Schedule: Section 1: MW 1:50 PM-3:10 PM (Remote)
Description: This course will introduce students to several classes of computational methods broadly referred to as "fast analysis-based algorithms" which exploit information about structure and symmetry to obtain more favorable computational complexity. Examples which will be discussed are butterfly algorithms, fast multiple methods, fast direct solvers, and hierarchical matrix compression. Though many of these algorithms first arose in physical applications such as simulating the motion of stars or the propagation of light and sound, they have subsequently found many fruitful applications in signal processing and data science.
Prerequisite(s): Familiarity with PDEs, analysis, and programming.