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
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- CountSketch, ℓ0 sampling, graph sketching.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 5
Amnesic dynamic programming (approximate distance to monotonicity). Hashing: cuckoo hashing analysis, power of two choices. CountMin sketch, point query,
Matrix completion.
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