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
Amnesic dynamic programming (approximate distance to monotonicity). Matrix completion. Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
CountSketch, ℓ0 sampling, graph sketching.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 21
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Krahmer-Ward proof, Iterative Hard Thresholding.
- Distinct elements, k-wise independence, geometric subsampling of streams.
- Competitive paging, cache-oblivious
- Scaling for max flow, blocking flow.
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