Introduction to Algorithms For Big Data Compsci 229r Lecture 21
Exploring Algorithms For Big Data Compsci 229r Lecture 21 reveals several interesting facts. ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
Algorithms For Big Data Compsci 229r Lecture 21 Comprehensive Overview
Krahmer-Ward proof, Iterative Hard Thresholding. Matrix completion. CountSketch, ℓ0 sampling, graph sketching.
Competitive paging, cache-oblivious
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 21
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- CountMin sketch, point query,
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- Scaling for max flow, blocking flow.
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