Introduction to Algorithms For Big Data Compsci 229r Lecture 23
Exploring Algorithms For Big Data Compsci 229r Lecture 23 reveals several interesting facts. External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
Algorithms For Big Data Compsci 229r Lecture 23 Comprehensive Overview
Competitive paging, cache-oblivious Matrix completion. Heavy
second order methods (Newton's method), path-following interior point wrap-up.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 23
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'
- ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2.
Stay tuned for more updates related to Algorithms For Big Data Compsci 229r Lecture 23.
