Welcome to the Code-DSA repository! This repository contains my implementations of various data structures and algorithms in Java. It serves as a learning resource for data structures and algorithms.
The repository is organized into different directories, each focusing on a specific topic. Here's an overview of the contents:
Arrays: Implementations and examples related to array manipulation, searching, and sorting algorithms.LinkedLists: Implementations of singly linked lists, doubly linked lists, and related operations.Stacks: Implementation of the stack data structure and related algorithms.Queues: Implementation of the queue data structure and related algorithms.Trees: Implementations of various tree data structures such as binary trees, binary search trees, and balanced trees, along with traversal algorithms.Graphs: Implementations of graph data structures and common algorithms like breadth-first search and depth-first search.Sorting: Implementations of various sorting algorithms like bubble sort, selection sort, merge sort, and quicksort.Searching: Implementations of searching algorithms like linear search, binary search, and interpolation search.DynamicProgramming: Implementations of dynamic programming algorithms for solving optimization problems.
Feel free to explore the directories and browse the code. Each directory contains detailed explanations, code examples, and notes to help you understand the concepts and algorithms better.
- O(1): Constant time complexity. The algorithm takes a constant amount of time to complete, regardless of the input size.
- O(log n): Logarithmic time complexity. The algorithm's running time increases logarithmically with the input size.
- O(n): Linear time complexity. The algorithm's running time increases linearly with the input size.
- O(n log n): Linearithmic time complexity. The algorithm's running time increases in proportion to the input size multiplied by the logarithm of the input size.
- O(n^2): Quadratic time complexity. The algorithm's running time increases quadratically with the input size.
- O(2^n): Exponential time complexity. The algorithm's running time grows exponentially with the input size.
- O(n!): Factorial time complexity. The algorithm's running time grows factorially with the input size.
These terms represent the rate at which the running time of an algorithm increases as the input size grows. It helps in analyzing and comparing the efficiency of algorithms.
- Brute Force: The simplest approach, trying all possible solutions, often with a nested loop.
- Divide and Conquer: Breaking down a problem into smaller subproblems, solving them recursively, and combining their solutions to get the final answer (e.g., Merge Sort, Quick Sort).
- Dynamic Programming: Breaking down a problem into smaller overlapping subproblems, solving them once and storing their solutions for future use to avoid redundant calculations (e.g., Fibonacci series, Knapsack problem).
- Greedy Algorithms: Making locally optimal choices at each step in the hope that it will lead to an optimal solution globally (e.g., Dijkstra's algorithm, Huffman coding).
- Backtracking: Exploring all possible solutions by incrementally building candidates and undoing choices when they are determined to be incorrect (e.g., N-Queens problem, Sudoku solver).
- Graph Algorithms: Algorithms that operate on graphs, such as finding the shortest path (e.g., Dijkstra's algorithm), detecting cycles (e.g., Depth-First Search), or finding minimum spanning trees (e.g., Kruskal's algorithm).
- Binary Search: An efficient search algorithm for sorted arrays by repeatedly dividing the search space in half (e.g., searching in a sorted array, finding a peak element).
- Heap and Priority Queue: Data structures that allow efficient insertion, deletion, and retrieval of elements with the highest (or lowest) priority (e.g., Heap Sort, Prim's algorithm).
- Hashing: Using a hash function to map data to an array index, providing fast retrieval, insertion, and deletion of elements (e.g., hash tables, hash-based set and map implementations).
- Two Pointers: Maintaining two pointers or indices to traverse an array or a linked list simultaneously, often used for searching, sorting, or dealing with two different parts of the data (e.g., two-sum problem, merging two sorted lists).
- Sliding Window: Maintaining a window of elements in an array or a substring in a string and efficiently updating it while iterating through the data (e.g., maximum sum subarray, longest substring without repeating characters).
- Graph Traversal: Visiting all vertices or nodes in a graph, typically using breadth-first search (BFS) or depth-first search (DFS).
This directory contains my solutions to various array-related problems. Each problem is implemented in Java and includes detailed explanations, code examples. The goal is to provide a comprehensive collection of array exercises to help you strengthen your understanding of array manipulation, searching, and sorting algorithms.
You can find the solutions to the array problems in the Arrays directory.
You can clone this repository to your local machine using the following command:
git clone https://github.com/hassanshahzadaheer/Code-DSA/
Once cloned, you can navigate to the respective directories to view the code files and accompanying explanations.
Contributions to this repository are welcome! If you have any improvements, bug fixes, or additional algorithms that you would like to share, feel free to open a pull request. Please make sure to follow the existing code style and provide clear explanations.
This repository is licensed under the MIT License. You are free to use the code for educational purposes and modify it according to your needs.
Happy coding and learning!