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. Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
Distinct elements, k-wise independence, geometric subsampling of streams.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 12
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- Competitive paging, cache-oblivious
- Matrix completion.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 12.