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.
- External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
- Amortized analysis, binomial heaps, Fibonacci heaps.
- Analysis of ℓp estimation
- More efficient exponential-time
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 8
Online CountSketch, ℓ0 sampling, graph sketching. Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
Splay trees.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 8.
