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
- Amnesic dynamic programming (approximate distance to monotonicity).
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- Distinct elements, k-wise independence, geometric subsampling of streams.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 5
Hashing: cuckoo hashing analysis, power of two choices. CountMin sketch, point query, P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
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