Understanding Algorithms For Big Data Compsci 229r Lecture 25
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 25. MapReduce: TeraSort, minimum spanning tree, triangle counting.
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 25
- Analysis of ℓp estimation
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Distinct elements, k-wise independence, geometric subsampling of streams.
- Power of random signs: ℓ2 norm estimation, subspace embeddings (regression), Johnson-Lindenstrauss, deterministic point ...
- P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 25
Competitive paging, cache-oblivious Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Matrix completion.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 25.
