Advanced Algorithmic Topics (ΠΛ3)

Graduate Courses Outlines - Department of Mathematics

General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Graduate
Course Code	ΠΛ3
Semester	1
Course Title	Advanced Algorithmic Topics
Independent Teaching Activities	Lectures (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type	Specialization
Prerequisite Courses	Undergraduate courses in Data structures and Algorithms (optionally a course in Discrete mathematics)
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English)
Course Website (URL)	See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes

The goal of this course is the deeper understanding of the design and analysis of algorithms and address specific classes of problems and algorithms to solve them as well as the introduction of students to critical thinking and research process. A detailed examination of advanced methods of analysis and design of algorithms is done during the course. The analysis of an algorithm studies ways of finding its complexity. For the design of an algorithm for a problem we discuss basic design methods such as: greedy methods, dynamic programming, backtracking, recursion, exhaustive search of solution space, and branch and bound. We examine algorithms for problem categories such as sorting, searching, selection, graphs processing, integers and polynomials arithmetic, algorithms in matrices, and string handling algorithms. Complexity classes such as P, NP, NP-complete are defined. Some specific topics are also presented. After completing the course the student: Can analyze an algorithm Can select the most effective algorithm between algorithms for solving a problem. Has a good understanding of design methods and can design efficient algorithms for solving a problem. Knows algorithms to solve basic categories of problems and can use them as a building block for the design of other algorithms.

General Competences

Independent work Bibliographic search Complexity analysis of an algorithm Effective selection of an algorithm to solve a problem Design of efficient algorithms for the solution of a problem

Syllabus

• Complexity of algorithms
• Asymptomatic complexity
• Complexity analysis of algorithms
• Methods of algorithm design (divide and conquer greedy method, dynamic programming, backtracking, recursion, exhaustive search, branch and bound, etc.)
• Problems categories and corresponding algorithms (sorting, searching, selection, graph algorithms, sorting networks, matrix algorithms, integers and polynomials arithmetic, string processing, computational geometry, etc.)
• Complexity classes P, NP, NP-complete, etc.
• Specific topics

Teaching and Learning Methods - Evaluation

Delivery	Face to face
Use of Information and Communications Technology	Yes
Teaching Methods	Activity Semester Workload Lectures 39 Self study 78 Exercises 70.5 Course total 187.5
Student Performance Evaluation	Final examination (40%) comprised of: questions about the design and analysis of algorithms and their properties questions requiring critical thinking Exercises: design, analysis, implementation, algorithm properties (30%). Presentations of related issues (30%).