Overview
Syllabus
Intro
Outline Background and Overview
RandNLA: Randomized Numerical Linear Algebra
Basic RandNLA Principles
Element-wise Sampling
Row/column Sampling
Random Projections as Preconditioners
Approximating Matrix Multiplication
Subspace Embeddings
Two important notions: leverage and condition
Meta-algorithm for E-norm regression (2 of 3)
Meta-algorithm for Iz-norm regression (3 of 3)
Least-squares approximation: the basic structural result
Least-squares approximation: RAM implementations
Extensions to Low-rank Approximation (Projections)
Taught by
Simons Institute