Understanding Algorithms For Big Data Compsci 229r Lecture 16
Exploring Algorithms For Big Data Compsci 229r Lecture 16 reveals several interesting facts. Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 16
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Competitive paging, cache-oblivious
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 16
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' P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Simplex wrap-up, strong duality, complementary slackness, ellipsoid, intro to interior point.
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