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
- 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.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- Hashing: cuckoo hashing analysis, power of two choices.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 5
CountMin sketch, point query, Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. Amnesic dynamic programming (approximate distance to monotonicity).
Krahmer-Ward proof, Iterative Hard Thresholding.
Stay tuned for more updates related to Algorithms For Big Data Compsci 229r Lecture 5.
