Exploring Algorithms For Big Data Compsci 229r Lecture 2
Welcome to our comprehensive guide on Algorithms For Big Data Compsci 229r Lecture 2.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Matrix completion.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- Amnesic dynamic programming (approximate distance to monotonicity).
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 2
Distinct elements, k-wise independence, geometric subsampling of streams. Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit. Competitive paging, cache-oblivious
Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
In summary, understanding Algorithms For Big Data Compsci 229r Lecture 2 gives us a better perspective.
