To study a variety of useful algorithms and analyze their complexity; by that experience to gain insight into principles and data-structures useful in algorithm design.
Methods for expressing and comparing complexity of algorithms: worst and average cases, lower bounds on algorithm classes, verification of correctness. Application of such analysis to variety of specific algorithms: searching, merging, sorting (including quick and heap internal and Fibonacci external sorts); graph problems (including connected components, shortest path, minimum spanning tree. and biconnected components); language problems (including string matching and parsing).
Consideration of a number of hard problems: knapsack, satisfiability, traveling salesman problems.
Development of NP-complete classification and its consequence.
Regular exercises, (about) 6 small programs
- will be prepared to contribute to a rapidly changing field by acquiring a thorough grounding in the core principles and foundations of computer science (e.g., techniques of program design, creation, and testing; key aspects of computer hardware; algorithmic principles).
- will acquire a deeper understanding on (elective) topics of more specialized interest, and be able to critically review, assess, and communicate current developments in the field.
- will be prepared for the next step in their careers, for example, by having done a research project (for those headed to graduate school), a programming project (for those going into the software industry), or some sort of business plan (for those going into startups).