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
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...
- Matrix completion.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
Detailed Analysis of Algorithms For Big Data Compsci 229r Lecture 10
Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma. Alon's JL lower bound, beyond worst case analysis: suprema of gaussian processes, Gordon's theorem. Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
Distinct elements, k-wise independence, geometric subsampling of streams.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 10.
