Understanding Algorithms For Big Data Compsci 229r Lecture 4
If you are looking for information about Algorithms For Big Data Compsci 229r Lecture 4, you have come to the right place. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 4
- Amnesic dynamic programming (approximate distance to monotonicity).
- Symmetrization, hashing: linear probing (5-wise indep.), bloom filters, cuckoo hashing, bloomier filters.
- Competitive paging, cache-oblivious
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- RIP and connection to incoherence, basis pursuit, Krahmer-Ward theorem.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 4
Analysis of ℓp estimation Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
We hope this detailed breakdown of Algorithms For Big Data Compsci 229r Lecture 4 was helpful.
