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Classroom Contents
Sampling for Linear Algebra, Statistics, and Optimization I
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- 1 Intro
- 2 Outline Background and Overview
- 3 RandNLA: Randomized Numerical Linear Algebra
- 4 Basic RandNLA Principles
- 5 Element-wise Sampling
- 6 Row/column Sampling
- 7 Random Projections as Preconditioners
- 8 Approximating Matrix Multiplication
- 9 Subspace Embeddings
- 10 Two important notions: leverage and condition
- 11 Meta-algorithm for E-norm regression (2 of 3)
- 12 Meta-algorithm for Iz-norm regression (3 of 3)
- 13 Least-squares approximation: the basic structural result
- 14 Least-squares approximation: RAM implementations
- 15 Extensions to Low-rank Approximation (Projections)
