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
External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Matrix completion. Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
CountMin sketch, point query,
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 21
- Krahmer-Ward proof, Iterative Hard Thresholding.
- Competitive paging, cache-oblivious
- CountSketch, ℓ0 sampling, graph sketching.
- Distinct elements, k-wise independence, geometric subsampling of streams.
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
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