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. Amnesic dynamic programming (approximate distance to monotonicity).
CountSketch, ℓ0 sampling, graph sketching.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 21
- Competitive paging, cache-oblivious
- Distinct elements, k-wise independence, geometric subsampling of streams.
- Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.
- Scaling for max flow, blocking flow.
- Krahmer-Ward proof, Iterative Hard Thresholding.
Stay tuned for more updates related to Algorithms For Big Data Compsci 229r Lecture 21.
