Understanding Algorithms For Big Data Compsci 229r Lecture 5
Exploring Algorithms For Big Data Compsci 229r Lecture 5 reveals several interesting facts. Analysis of ℓp estimation
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 5
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Amnesic dynamic programming (approximate distance to monotonicity).
- Matrix completion.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 5
P-stable sketch analysis, Nisan's PRG, ℓp estimation for p CountMin sketch, point query, Competitive paging, cache-oblivious
Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
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