Sampling for Linear Algebra, Statistics, and Optimization I

Sampling for Linear Algebra, Statistics, and Optimization I

Simons Institute via YouTube Direct link

Intro

1 of 15

1 of 15

Intro

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Sampling for Linear Algebra, Statistics, and Optimization I

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

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