Instructors: Prof. Rana Barua and Dr. Nilanjan Datta

Course Objective:

The course introduces the basics of computational complexity analysis and various algorithm design paradigms. The goal is to provide students with solid foundations to deal with a wide variety of computational problems, and to provide a thorough knowledge of the most common algorithms and data structures. After the course, a student should be able to analyze the asymptotic performance of algorithms, write rigorous correctness proofs for algorithms, and apply important algorithmic design paradigms and methods of analysis.

  • Introduction: Algorithm, Instance of a problem, Efficiency of algorithm, Growth of Functions, Asymptotic notation, Worst case, Best case, Average Case time complexity, Substitution method, Recursion tree method, Masters Theorem.
  • Elementary Data Structure: Array, Linked list, Stack, Queue, Heap, Binary Search Tree, AVL Tree, Hash table, Disjoint Set Data Structure.
  • Searching, Sorting and Order Statistics: Linear and Binary Search, Heap Sort, Quick Sort, Sorting in linear time, Order statistics, Finding Median in linear time.
  • Divide and Conquer Paradigm: Merge Sort, Counting Inversion, Closest Pair of Points.
  • Greedy Algorithms: Interval Scheduling Problem and its variants, Optimal Caching Problem, Minimum Spanning tree Problem, Huffman
    Code, Clustering Problem, Fractional Knapsack problem, Dijkstra Algorithm.
  • Dynamic Programming: Matrix Chain Multiplication, Longest Common Subsequence, Optimal Binary Search Tree, Segmented Least Square Problem, 0/1-Knapsack Problem, Subset Sum Problem, Bellman Ford Algorithm.
  • Graph algorithms: BFS, DFS, Floyd Warshall, Fold Fulkerson.
  • Number Theoretic Algorithms: Euclidean, Extended Euclidean, CRT, Pollard Rho.
  • Advanced Topics: P, NP, NPC (Circuit Satisfiability, Vertex Cover, Graph Coloring), Approximation Algorithm of some NPC Problems, Probabilistic Algorithm: Miller Rabin Primality Algorithm.


[1] T. H. Cormen, C. E. Leiserson and R. L. Rivest: Introduction to Algorithms, PrenticeHall of India, New Delhi, 1998.
[2] J. Kleinberg, E. Tardos: Algorithm Design, Pearson Education, 2006.
[3] A. Aho, J. Hopcroft and J. Ullman: The Design and Analysis of Computer Algorithms, A. W. L, International Student Edition, Singapore, 1998.
[4] E. Horowitz, S. Sahni, S. A. Freed: Fundamentals of Data Structures in C, 2008.
[5] S. Baase: Computer Algorithms: Introduction to Design and Analysis, 2nd ed., Addison-Wesley, California, 1988.



Implement Wordle and design an algorithm to solve Wordle efficiently.

Project Presentation: 07/06/2022

Class Test on 29/03/2022 from 2:30 PM [Topic: Sorting and Order Statistics]

Mid-Semester Examination will be held on 11/04/2022 [Syllabus: Elementary Data Structures, Searching, Sorting and Order Statistics, Divide and Conquer Paradigm, Greedy Algorithms, Dynamic Programming]

Classes (by Dr. Nilanjan Datta):

  • Introduction to Algorithms, Insertion Sort, Correctness using Loop Invariants, Analysis of Algorithms: Best Case, Average Case, Worst Case Analysis. [Class 1]

  • Growth of Function, Asymptotic Notation, Introduction to Data Structures, Difference between Interface and Data Structure, Static Sequence Interface, Static Array. [Class 2]

  • Dynamic Sequence Interface, Dynamic Array, Linked List, Doubly Linked List, A comparative study of Static Array, Linked List, and Dynamic Array with respect to the efficiency of different static and dynamic operations. [Class 3]

  • Data Structures to represent Polynomials; Stack Data Structure and Its applications: Infix to Postfix Conversion, Evaluation of Postfix expression, Finding Minimum elements in the stack efficiently. [Class 4]

  • Queue, Circular Queue, Priotity Queue, Heap Data Structure, Max-heap, Min-heap, Heap Sort, Implementation of Priority Queue using Heap. [Class 5]

  • Fibonacci Heap, Priority Queue using Fibonacci Heap; Searching: Linear and Binary Search, Binary Search Tree, Motivation for Balanced Binary Search Tree. [Class 6]

  • Balanced Binary Search Tree, AVL tree, Red-Black tree. [Class 7]

  • Quick Sort, Randomized Quick Sort, Expected Running Time of Randomized Quick Sort, Comparison based Sort, Decision Tree, Lower Bound on Comparison based Sort. [Class 8]

  • Sorting in Linear Time: Counting Sort, Stable Sort, Radix Sort, Bucket Sort. [Class 9]

  • Medians and Order Statistics: Minimum and Maximum, Selection in expected linear time, Selection in worst case linear time. [Class 10]

  • Dynamic Sets with Dictionary Operations: Direct Address Table, Hash Table, Collision Resolution by Chaining, Open Addressing: Linear and Quadratic Probing, Perfect Hashing. [Class 11]

  • Dynamic Programming: Optimal Binary Search Trees, Coin Game. [Class 12]

Classes (by Prof. Rana Barua):

  • Divide and Conquer Paradigm: Merge Sort, Counting Inversions, Closest Pair of Points. [Class 1]

  • Greedy Algorithms (part I): Interval Scheduling Problem – EFT Algorithm, Minimum Spanning Tree Construction – Prim’s Algorithm. [Class 2]

  • Greedy Algorithms (part II): Minimum Spanning Tree Problems: Prim’s Algorithm, Kruskal’s Algorithm, Use of union-find data structure, Clustering Problem. [Class 3]

  • Greedy Algorithms (part III): Clustering Problem, Huffman Coding. [Class 4]

  • Greedy Algorithms (part IV): Huffman Algorithm, Fractional Knapsack, Single-source Shortest Path, Dijkstra’s Algorithm. [Class 5]

  • Dynamic Programming (part I): Longest Common Sequence, Matrix Chain Multiplication. [Class 6]

  • Dynamic Programming (part II): Matrix Chain Multiplication, Subset Sum and Knapsack. [Class 7]

  • Number Theoretic Algorithms (part I): Euclidean, Extended Euclidean, Chinese Remainder Theorem. [Class 8]

  • Number Theoretic Algorithms (part II): Pollard’s Rho Factoring Algorithm, Miller-Rabin Algorithm for Primality Testing. [Class 9]

Notes (by Prof. Rana Barua):