# ITGS301 : Design and Analysis algorithms

### Department

Department of Information Systems

Bachelor in Information Systems

General

03

ITGS220

### Overview

The module introduces formal techniques to support the design and analysis of algorithms, focusing on both the underlying mathematical theory and practical considerations of efficiency. Topics include asymptotic complexity bounds, techniques of analysis, and algorithmic strategies.

### Knowledge &understand

• Learn how to analyze algorithms and compute running time.
• Learn about asymptotic analysis of algorithms.
• To understand the concepts of algorithm design
• To recognize the classification of recursive and non-recursive algorithms
• Explain the basic model of divide-and-conquer approach, some examples.
• Recognize the methods of analyzing the execution time of recursive algorithms
• To become familiar with the better use of the concept of dynamic programming

### mental skills

• To distinguish between (Big O, Big Omega, & Big Theta) Asymptotic notations
• To choose the best algorithm to solve a problem.
• To distinguish between recursive algorithms & Non recursive algorithms
• To compare the execution time of the different algorithms.

### Practical & professional skills

• Analysis of the running time of different algorithms
• Analyzing the complexity of an algorithm using asymptotic notations.
• Determine the efficiency of an algorithm, and compared it to others to solve a problem.
• Use different concepts in designing algorithms

### General and transferable skills

• To be able to use modern technological tools.
• Able to using internet and scientific references for independent study
• Able to using internet and scientific references for independent study
• To be able to do presentations

### Teaching and learning methods

• Lectures
• Tutorials
• Problem-based learning

### Methods of assessments

• Midterm exam = 40
• Assignment = 10
• Final exam = 50

### Course contents

• Introduction of algorithms
• Performance analysis
• Asymptotic notations
• Complexity and Orders of Growth
• Analysis of time Complexity
• Sorting problem : Insertion sort
• RECURRENCE RELATIONS:
• SOLVING RECURRENCES Iteration method
• The master method for solving recurrences
• The recursion-tree method for solving recurrences
• Designing Algorithms / Divide-and-Conquer
• Merge sort and Quick sort-Complexity
• Dynamic Programming
• Elementary Graph Algorithms
• Greedy Algorithms
• Single-Source Shortest Paths