Please check*homework* page frequently for clarifications
regarding assignments, and any late-breaking news regarding due dates
etc.

A **Style Guide** is available
on-line to assist you in determining the correct style for your
programs. You are required to follow the guidelines in all programs
you turn in for the course. Failure to follow the guidelines may
result in a significantly lower grade on an assignment.

**Instructor:**David M. Chelberg (Press here to email)**Office:**Stocker 322B**Office Hours:**Will be via Microsoft Teams (see the office hours channel for more details.)-
**Grader: There will be one grader for the course (tbd)** **Homework assignments**-
**Reading assignments** **Lecture Notes**- Lecture notes will be available from Blackboard.
**Objectives**- This course provides an introduction to the modern study
of computer algorithms. Through this course students should
be able to:
- analyze algorithm performance using complexity measurement.
- master major algorithms design techniques such as divide and conquer, greedy and dynamic programming.
- apply above approaches to solve a variety of practical problems such as sorting and selection, graph problems, computational geometry, etc.
- understand the theory of NP-completeness.

**Prereq:**- CS3610 and CS3000.
**Required Texts:**-
"Introduction to Algorithms, Third Edition," by Cormen,
Leiserson, Rivest, and Stein, MIT Press, 2009.

**Recommended Texts:**- "Computer Algorithms, 2ed" by Horowitz, Sahni and Rajasekaran,
Silicon Press 2008.
**Course Outline:**- This course emphasizes the importance of fundamental analysis and
design techniques to programming. Here we build on CS 3610 (Data
Structures and Algorithms) and examine the following topics in
more detail.
- The mathematical analysis of algorithms: best-case, worst-case, average case analysis; asymptotic growth rates; recurrence relations.
- Algorithms design techniques: divide and conquer algorithms; greedy algorithms; dynamic programming algorithms.
- Lower bound analysis.
- Intractability: P vs. NP vs. NP-completeness.
- Special topics (e.g. Mathematical algorithms: fast Fourier transform; matrix multiplication).

**Expectations**- Students are expected to spend AT LEAST two hours outside of class per class session, including working exercises in the book, and programming homework problems. Programming can only be learned by doing! In this class students are expected to write many programs in order to gain proficiency, and to fully understand the algorithms and data structures covered.
**Examination schedule:**- There will be one midterm exam (tbd) . Blackboard quizzes will also be used to assess your progress in the course.
**Grading policy:**- Your grade will be based on a composite score computed according to the following approximate breakdown: 20% for quizzes, 25% for paper homework and programming projects 20% for the midterm, and 35% for the final.
**Attendance Policy:**- Students are strongly encouraged to attend all classes, but attendance is not required. Class attendance will not be used in the final determination of grades. Students miss classes at their own risk. There will be no make-up quizzes, students missing class on the day of a quiz will be given a zero. Students are required to attend class during the midterm and final exam unless prior arrangements have been made.
**Academic dishonesty:**- Students are expected to turn in only their own work with proper documentation. Anything else will result in an F for the exam, project or program, and possibly an F for the course, or even dismissal from the University. This means NO WORKING IN GROUPS, and NO SHARING CODE. For more information see the student affairs handbook
**Interesting Links:**- An article outlining some dangers of algorithms that get out of control.
- Limit Tests for Big O, Big Theta, Big Omega and little o, and omega.
- A Wikipedia article on Big O, etc. notation with hints.
- Logarithm Facts
- Counting Sort Visualization from class.
- Sorting animations
- Another visualization of sorting algorithms.
- You tube video "I will derive" Humorous!
- Zune Bug Discussion (algorithm correctness, etc.)
- Article Titled: "How Speeding The "Most Important Algorithm Of Our Lifetime" Could Change This Modern World"
- Computational Complexity applied to video games, this paper proves PacMan is NP-Hard.
- Sample Proofs using mathematical induction
- Sorting Algorithms as Dances.

David M. Chelberg <chelberg@ohio.edu> last-modified: Thu Jul 22 12:16:07 2021