Analysis of Algorithms

Rigorous analysis of the time and space requirements of important algorithms, including worst case, average case, and amortized analysis.  Techniques include order-notation, recurrence relations, information-theoretic lower bounds, adversary arguments. Analysis of the key data structures: trees, hash tables, balanced tree schemes, priority queues, Fibonacci and binomial heaps. Algorithmic paradigms such as divide and conquer, dynamic programming, union-find with path compression, augmenting paths. Selected advanced algorithms. Introduction to NP-completeness. (Prerequisites: Restricted to graduate students; undergraduate students may enroll in this course with the appropriate additional prerequisites and the consent of the instructor.)