Algorithm Design, Analysis, and Implementation


Credit Hours:


Learning Objective:

To learn about and be able to apply data structures techniques and algorithm design and analysis techniques as the graduate level. To understand and construct lower bound proofs, as well as prove membership in problem classes such as NP-complete and PSPACE-complete.


Basic techniques for designing and analyzing algorithms: dynamic programming, divide and conquer, balancing. Upper and lower bounds on time and space costs, worst case and expected cost measures. A selection of applications such as disjoint set union/find, graph algorithms, search trees, pattern matching. The polynomial complexity classes P, NP, and co-NP; intractable problems.

Topics Covered:

Recurrence Relations
Prune and Search, and Divide and Conquer
Dynamic Programming
Data Structures including Fibonacci heaps, disjoint sets
Graph Algorithms including max flow (Goldberg-Tarjan)
Lower Bound Techniques
NP-complete Problems including approximation algorithms
PSPACE-complete Problems
Randomized Algorithms


A bachelor degree in computer science or an equivalent field. Students not in the Computer Science master's program should seek department permission to register.
An undergraduate course in the analysis of algorithms, and an undergraduate course in the discrete mathematics used in computer science.

Applied / Theory:

30 / 70

Web Address:

Web Content:

Syllabus, grades, homework assignments, solutions, chat room and message board.


Seven to ten written assignments. These assignments will generally involve the design and analysis of efficient algorithms and may require substantial thought. Approximately 10 hours per week on average.




A midterm exam near the middle of the course, and a final exam.


Tentative: Algorithm Design; Jon Kleinberg; Pearson; 1st Edition; ISBN: 9780321295354

Computer Requirements:

ProEd minimum computer requirements.

ProEd Minimum Requirements:


Tuition & Fees: