Data Structures & Algorithms
DSA Part-1
- Introduction to Data Structures
- Asymptotic Notations: Big-O, Omega, and Theta Notations
- Time Complexity and Space Complexity Analysis of Algorithms
- 1D Arrays: Memory Representation and Address Calculation
- 2D Arrays: Row-Major and Column-Major Order Formulas and Numericals
- Array Operations: Traversal, Insertion, and Deletion with Algorithms
- Sparse Matrix: Triplet Representation and Linked Representation
- String Operations, Memory Storage, and Pattern Matching Basics
- Linear Search Algorithm and Complexity Analysis
- Binary Search Algorithm (Iterative and Recursive)
- Bubble Sort Algorithm and Optimization
- Selection Sort Algorithm and Step-by-Step Trace
- Insertion Sort Algorithm and In-Place Analysis
- Merge Sort Algorithm (Divide and Conquer Approach)
- Quick Sort Algorithm and Partitioning Methods
- Counting Sort Algorithm (Non-Comparison Sorting)
- Radix Sort Algorithm and Bucket Distribution
DSA Part-2
- Singly Linked List: Concept and Memory Allocation
- Singly Linked List: Insertion Operations
- Singly Linked List: Deletion Operations
- Singly Linked List: Reversal and Search Operations
- Doubly Linked List: Node Structure and Traversal
- Doubly Linked List: Insertion and Deletion Operations
- Circular Singly Linked List: Concept and Operations
- Circular Doubly Linked List: Concept and Operations
- Header Linked List: Grounded and Circular Header Lists
- Polynomial Representation and Addition using Linked List
DSA Part-3
- Stack Data Structure: Array Implementation
- Stack Implementation using Linked List
- Infix, Prefix, and Postfix Notations
- Infix to Postfix Conversion using Stack
- Infix to Prefix Conversion using Stack
- Evaluation of Postfix Expression using Stack
- Recursion and System Stack (Tower of Hanoi)
- Linear Queue: Array Implementation and Operations
- Drawbacks of Linear Queue and Memory Wastage
- Circular Queue: Array Implementation and Condition Checks
- Queue Implementation using Linked List
- Double-Ended Queue (Deque): Input and Output-Restricted
- Priority Queue: Concept, Array and Heap Representation
DSA Part-4
- Tree Data Structure: Terminology and Basic Concepts
- Binary Trees: Types and Properties
- Binary Tree Traversals: Inorder, Preorder, and Postorder
- Level Order Traversal of Binary Tree
- Binary Search Tree (BST): Concept and Search Operation
- Binary Search Tree (BST): Insertion and Deletion Operations
- AVL Tree: Concept and Balance Factor
- AVL Tree Rotations: LL, RR, LR, and RL Rotations
- Binary Heap: Min-Heap and Max-Heap Properties
- Heap Operations: Heapify, Insertion, and Deletion
- B-Tree: Properties, Structure, and Insertion
- B+ Tree: Structure and Comparison with B-Tree
DSA Part-5
- Graph Data Structure: Terminology and Types
- Graph Representation: Adjacency Matrix and Adjacency List
- Breadth First Search (BFS) Algorithm for Graphs
- Depth First Search (DFS) Algorithm for Graphs
- Hashing: Hash Tables and Hash Functions
- Collision Resolution: Open Addressing Techniques
- Collision Resolution: Separate Chaining Method
Breadth First Search (BFS) Algorithm for Graphs
Breadth First Search (BFS) graph traversal ka ek foundational algorithm hai jo source vertex se start karke pehle sabhi immediate neighbor vertices (distance 1) ko explore karta hai, fir distance 2 ke vertices ko, and so on level by level. BFS ek FIFO Queue aur ek Visited Boolean Array use karta hai taaki cycles me infinite loop na bane.
BFS Algorithm
BFS(G, startVertex)
1. Initialize visited[V] = {FALSE}
2. Create empty Queue Q
3. Mark visited[startVertex] = TRUE
4. ENQUEUE(Q, startVertex)
5. While Q is not empty do:
6. u = DEQUEUE(Q)
7. Print u
8. For each neighbor v adjacent to u do:
9. If visited[v] == FALSE then:
10. visited[v] = TRUE
11. ENQUEUE(Q, v)
12. End While
Graph:
(0)
/ \
(1) (2)
| |
(3) (4)
BFS Traversal starting from 0:
Level 0: 0
Level 1: 1, 2
Level 2: 3, 4
Output: 0 -> 1 -> 2 -> 3 -> 4
Complexity and Applications
- Time Complexity:
O(V + E)jab Adjacency List use hoti hai;O(V^2)jab Adjacency Matrix use hoti hai. - Space Complexity:
O(V)visited array aur queue memory ke liye. - Key Applications: Unweighted graph me Shortest Path find karna, Connected Components count karna, Cycle detection in undirected graphs, Bipartite graph verification.
Agli post me hum cover karenge Depth First Search (DFS) Algorithm for Graphs.