Course

Unit I

Algorithms vs Flowcharts, Characteristics of algorithms, Algorithm representation techniques, Time and Space Complexity Best, Average, and Worst‑case analysis, Asymptotic Notations: Big‑O, Big‑Ω, Big‑Θ.

Unit II

Algorithm analysis methodology. Analyzing iterative programs. Simple algorithm analysis examples. Comparative analysis of algorithms. Performance measurement techniques

Unit III

Introduction to recursion. Examples: Tower of Hanoi. Factorial, Fibonacci. Analysis of recursive algorithms. Back Substitution method. Master’s Theorem. Divide and Conquer approach.

Unit IV

Searching algorithms: Linear Search (analysis), Binary Search (analysis). Sorting algorithms: Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort. Comparative analysis of sorting algorithms.

Unit V

Brute Force technique. Divide and Conquer strategy. Greedy method (introductory examples). Limitations of algorithms and trade‑offs.

Course Objective(s) 

• Introduce algorithmic thinking and problem‑solving strategies. 
• Develop the ability to analyze algorithm efficiency using complexity measures. 
• Enable students to design algorithms using standard paradigms. 
• Train students to evaluate and compare algorithms based on performance and applicability. 

Course Outcomes 

CO-PO Mapping 

• Analysis of Algorithms, Jeffrey J McConnel, Jones and Bartlett Publishers, Inc, 2nd Revised edition, 2 November 2007
• Introduction to the Design and Analysis of Algorithms, Anany Levitin, Third Edition, Pearson Education, 2012
• Introduction to Algorithms, Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein. Third Edition, Prentice-Hall of India Private Limited; 2009.