Understanding Algorithms For Big Data Compsci 229r Lecture 10
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 10. Randomized and approximate F0 lower bounds, disjointness, Fp lower bound, dimensionality reduction (JL lemma).
Key Takeaways about Algorithms For Big Data Compsci 229r Lecture 10
- Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- Amnesic dynamic programming (approximate distance to monotonicity).
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
- Analysis of ℓp estimation
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 10
Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting. Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
Competitive paging, cache-oblivious
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 10.