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
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- Competitive paging, cache-oblivious
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 16
P-stable sketch analysis, Nisan's PRG, ℓp estimation for p Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression. Simplex wrap-up, strong duality, complementary slackness, ellipsoid, intro to interior point.
MapReduce: TeraSort, minimum spanning tree, triangle counting.
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