Title: Randomized Algorithms in Linear Algebra


Speaker: Ravi Kannan

Date: August 11
Time: 11:10 - 12:10
Room: Ballroom C

Chair: Song Jiang


Abstract: A small random sample of rows/columns of any matrix is a decent proxy for the matrix, provided sampling probabilities are proportional to squared lengths. Since the early theorems on this from the 90's, there has been a substantial body of work using sampling (random projections and probabilties based on leverage scores are two examples) to reduce matrix sizes for Linear Algebra computations. The talk will describe theorems, applications and challenges in the area.

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