Understanding Algorithms For Big Data Compsci 229r Lecture 7
Exploring Algorithms For Big Data Compsci 229r Lecture 7 reveals several interesting facts. CountSketch, ℓ0 sampling, graph sketching.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 7
- Hashing: cuckoo hashing analysis, power of two choices.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Matrix completion.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Linear programming via multiplicative weights, flows, augmenting paths.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 7
Amnesic dynamic programming (approximate distance to monotonicity). Splay trees. CountMin sketch, point query,
Analysis of ℓp estimation
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