This repository contains implementations of various Data Structures and Algorithms in C++. It is intended for my personal revision and future reference as I continue learning and improving my DSA skills.
The goal of this repository is to:
- Practice and improve problem-solving skills using C++.
- Maintain a collection of commonly used algorithms and data structures for quick reference.
- Document solutions to DSA problems in a structured and reusable manner.
- Arrays
- Strings
- Linked Lists
- Stacks and Queues
- Trees
- Graphs
- Hash Tables
- Recursion and Backtracking
- Divide and Conquer
- Greedy Algorithms
- Dynamic Programming
Data Structures Data structures are broadly categorized into Linear and Non-Linear types. Linear structures like arrays, linked lists, stacks, and queues organize elements sequentially, enabling ordered access. Non-linear structures such as trees and graphs are hierarchical or interconnected, offering flexible relationships between elements. Specialized structures like heaps (used for priority tasks) and tries (efficient for string searches) further enhance problem-solving capabilities. Hash-based structures, such as hash tables, provide quick data retrieval using key-value pairs.
Algorithms Algorithms form the core of problem-solving in DSA. Sorting algorithms (e.g., Bubble Sort, Quick Sort) rearrange elements systematically, while searching algorithms (e.g., Binary Search) quickly locate specific items. Graph algorithms (like DFS, BFS, and Dijkstra’s) solve connectivity and pathfinding problems. Dynamic Programming (DP) and Greedy algorithms tackle optimization by breaking problems into smaller parts or making immediate decisions. Techniques like divide-and-conquer and backtracking systematically explore solutions. String algorithms, such as KMP and Rabin-Karp, are optimized for text operations.
Complexity Analysis Every algorithm is evaluated based on its time complexity (execution speed) and space complexity (memory usage). Common time complexities range from O(1) (constant time) to O(n²) (quadratic time), offering insights into efficiency. Understanding these metrics helps choose the right approach for different scenarios.