East Carolina University
Department of Computer Science

CSCI 3650
Design and Analysis of Algorithms
Standard Syllabus

Catalog entry

P: CSCI 2530. Formal techniques to support the design and analysis of algorithms. Asymptotic complexity bounds, techniques of analysis, and algorithmic strategies.

Course summary

This course applies techniques and knowledge gained from the discrete mathematics and data structures courses to fundamentals of algorithm design and analysis. The focus is on algorithms that provide an efficient solution to a computational problem. Efficiency refers to optimizing the time and space cost required during execution of the algorithm. While algorithms are ultimately implemented as computer programs, design and analysis fundamentals are independent of any particular implementation language.

Course topics

• Algorithm design techniques such as divide and conquer, dynamic programming, greedy algorithms, and scan algorithms.
• Algorithm analysis techniques including asymptotic notation and expressing and solving elementary summations and recurrences.
• Survey of known algorithms such as heapsort, quicksort, shortest paths, minimum spanning trees, and elementary graph algorithms.
• Brief coverage of advanced analysis techniques such as lower bounds and NP-Completeness.

Student learning outcomes

• Determine and express the time and space cost of an algorithm as a function of the problem size.
• Apply common techniques such as divide-and-conquer and dynamic programming to the design of correct and efficient algorithms for given computational problems.
• Explain the fundamental properties of known efficient algorithms for some important computational problems.

Textbook

Introduction to Algorithms , Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, 3rd edition, MIT Press, 2009.

Encylopedic coverage of algorithms that can also serve as a reference for a computing professional. After careful coverage of the first four chapters, instructors can pick and choose from many different topics. Common choices have included sorting, dynamic programming and greedy algorithms, graph algorithms, computational geometry, and NP-completeness fundamentals.

Grading

Grading is a based on a combination of homework assignments, quizzes/exams, and a comprehensive final exam. Representative weightings are 25% for homework, 50% for the quizzes/exams, and 25% for the final exam.

Grade meanings