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