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. Amnesic dynamic programming (approximate distance to monotonicity).
ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.
Summary & Highlights for Algorithms For Big Data Compsci 229r Lecture 23
- Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.
- MapReduce: TeraSort, minimum spanning tree, triangle counting.
- Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.
- Learning from experts, multiplicative weights.
- Path-following interior point, first order methods (gradient descent).
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