Introduction to Algorithms For Big Data Compsci 229r Lecture 12
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 12. Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
Algorithms For Big Data Compsci 229r Lecture 12 Comprehensive Overview
Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. ORS theorem (distributional JL implies Gordon's theorem), sparse JL. Competitive paging, cache-oblivious
Amnesic dynamic programming (approximate distance to monotonicity).
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 12
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 12.
