Exploring Algorithms For Big Data Compsci 229r Lecture 15
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- Analysis of ℓp estimation
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 15
Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings. Distinct elements, k-wise independence, geometric subsampling of streams. Amnesic dynamic programming (approximate distance to monotonicity). Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
MapReduce: TeraSort, minimum spanning tree, triangle counting.
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