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
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Analysis of ℓp estimation
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 7
Amnesic dynamic programming (approximate distance to monotonicity). CountMin sketch, point query, Splay trees.
Amortized analysis, binomial heaps, Fibonacci heaps.
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