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|Title:||Randomized Algorithms (PDF)|
|Authors:||R. Karger, Prof. David|
|Description:||This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.|
|Appears in Collections:||Mathematics & statistics|
Mathematics & statistics
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