Understanding Algorithms For Big Data Compsci 229r Lecture 8
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 8. Amnesic dynamic programming (approximate distance to monotonicity).
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 8
- Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- Analysis of ℓp estimation
- Competitive paging, cache-oblivious
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 8
Online External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Matrix completion.
Krahmer-Ward proof, Iterative Hard Thresholding.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 8.