Understanding Algorithms For Big Data Compsci 229r Lecture 8
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 8. Amnesic dynamic programming (approximate distance to monotonicity).
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 8
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- Krahmer-Ward proof, Iterative Hard Thresholding.
- Matrix completion.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 8
Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds. CountSketch, ℓ0 sampling, graph sketching. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Competitive paging, cache-oblivious
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 8.