This repository contains implementations and explanations of various data structures and algorithms commonly used in computer science and programming. Understanding these fundamental concepts is essential for developing efficient and optimized software solutions.
- Data Structures
- Arrays
- Linked Lists
- Stacks
- Queues
- Trees
- Graphs
- Hash Tables
- Algorithms
- Searching Algorithms
- Sorting Algorithms
- Graph Algorithms
- Dynamic Programming
- Recursion
Arrays are contiguous blocks of memory used to store multiple elements of the same type. They provide constant-time access to elements via indexing.
Linked lists are linear data structures where elements are stored in nodes. Each node contains a data field and a reference (or pointer) to the next node in the sequence.
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. Elements are inserted and removed from the same end, typically referred to as the top of the stack.
A queue is a linear data structure that follows the First In, First Out (FIFO) principle. Elements are inserted at the rear and removed from the front.
Trees are hierarchical data structures consisting of nodes connected by edges. Each node has a parent and zero or more children. Common types include binary trees, binary search trees, and AVL trees.
Graphs consist of vertices (nodes) and edges (connections between nodes). They can be directed or undirected, weighted or unweighted. Graphs are used to model relationships between objects.
Hash tables (or hash maps) are data structures that store key-value pairs. They use a hash function to compute an index where an element can be stored or retrieved efficiently.
Searching algorithms are used to find a particular element within a data structure. Common searching algorithms include linear search, binary search, and depth-first search (DFS), breadth-first search (BFS) for graphs.
Sorting algorithms are used to rearrange elements in a specific order, such as numerical or lexicographical. Common sorting algorithms include bubble sort, insertion sort, merge sort, quick sort, and heap sort.
Graph algorithms operate on graphs to solve various problems. Examples include finding the shortest path (Dijkstra's algorithm), detecting cycles (Depth-First Search), and finding minimum spanning trees (Prim's algorithm, Kruskal's algorithm).
Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It often involves solving each subproblem only once and storing the solutions to avoid redundant computations.
Recursion is a programming technique where a function calls itself to solve a problem. It is commonly used in algorithms involving trees, graphs, and divide-and-conquer strategies.
Contributions to this repository are welcome! If you have additional implementations, optimizations, or explanations for data structures and algorithms, feel free to submit a pull request.
This project is licensed under the MIT License. Feel free to use, modify, and distribute the code for both commercial and non-commercial purposes.