Course 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.
Instructor:  
Course Number: 

Departments:  
Topics:  
As Taught In:  Fall 2002 
Level: 
Graduate

Learning Resource Types
notes
Lecture Notes
assignment_turned_in
Problem Sets with Solutions