Understanding Algorithms For Big Data Compsci 229r Lecture 5
Exploring Algorithms For Big Data Compsci 229r Lecture 5 reveals several interesting facts. Analysis of â„“p estimation
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 5
- Amnesic dynamic programming (approximate distance to monotonicity).
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- CountSketch, â„“0 sampling, graph sketching.
- P-stable sketch analysis, Nisan's PRG, â„“p estimation for p
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 5
Hashing: cuckoo hashing analysis, power of two choices. Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. CountMin sketch, point query,
Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
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