CSCI 256: Spring 2021

Algorithm Design and Analysis

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Course Information

Course Description and Learning Goals

This course is about mathematical modeling of computational problems, developing common algorithmic techniques to solve them, and about analyzing the correctness and running time of the algorithms. By clearly formulating and carefully analyzing the structure of a problem, it is often possible to dramatically decrease the computational resources needed to solve it. In addition, by analyzing algorithms you can provide provable guarantees of their performance. We will study several algorithm design strategies that build on data structures and programming techniques introduced in CS 136 and mathematical tools introduced in MATH 200. The course will roughly cover the following topics in order: graph algorithms, greedy algorithms, divide-and-conquer, dynamic programming, NP completeness and problem reductions, approximation algorithms and randomized algorithms. At the end of the course, the students should be able to:

• Analyze worst-case running time and space usage of algorithms using asymptotic analysis.
• Formulate real-world optimization problems mathematically (using concepts like sets and graphs) and apply algorithmic paradigms such as divide-and-conquer and dynamic programming to solve them.
• Identify and prove that certain computational problems are NP-hard or NP-complete, that is, show that they are unlikely to admit an efficient solution.
• Design and analyze simple randomized algorithms for computational problems.

Readings

The primary text for the course is Algorithm Design by Jon Kleinberg and Éva Tardos, Addison-Wesley 2006. This will be supplemented by readings from the Algorithms textbook by Jeff Erickson freely available online. The course schedule describes the planned topics for each lecture and associated readings.

Course Requirements, Evaluation and Grading

Students will be evaluated based upon their overall performance in the course, according to the table above. Exams and problem sets will be graded on the correctness, clarity, thoroughness, and appropriateness of responses.

Problem Sets. Much of the learning in this course happens through practice. Weekly problem sets will be assigned as homework. The assignments page provides more information about the preparation and submission of problem sets. The course schedule provides the planned release/due dates of assignments. The course policies page provides detailed collaboration policy with respect to assignments.

Class Participation. Learning is a collaborative endeavor and class participation is encouraged and rewarded in this class. Participation can take various forms such as coming to class prepared, staying engaged during lectures, answering and asking questions, attempting optional challenge problems on problem sets, keeping me informed of class absences, and showing up to instructor/TA office hours.

Active class participation does not mean dominating discussions, or interjecting your peers but being a thoughtful participant in creating a vibrant, positive and inclusive classroom environment.

Midterm and Final Exam. The midterm exam will be a scheduled open-book take-home exam. The final exam will be a cumulative self-scheduled open-book take-home exam held during the final exam period.

Course Calendar

Special Accommodations

Students with disabilities of any kind who may need accommodations for this course are encouraged to contact Dr. G.L. Wallace (Director of Accessible Education) at 597-4672. Also, students experiencing mental or physical health challenges that are significantly affecting their academic work or well-being are encouraged to contact me and to speak with a dean so we can help you find the right resources. The deans can be reached at 597-4171. If you need accommodations for religious observances, you are encouraged to reach out to me. However, last-minute requests are unlikely to be granted.