Big O Notation

In computer science and algorithm analysis, Big O notation is used to describe the performance or complexity of an algorithm. It provides an upper bound on the growth rate of the time (or space) complexity of an algorithm in terms of the input size. It helps in understanding how the algorithm's performance scales with the size of the input. Common notations in Big O analysis include O(1), O(log n), O(n), O(n log n), O(n^2), O(2^n), and O(n!). Developers use Big O notation to evaluate the efficiency of an algorithm and to make informed decisions when choosing between different algorithms or approaches to solve a problem. Understanding Big O notation is crucial for writing efficient and scalable code, especially when dealing with large-scale data or time-sensitive applications.

LeetCode

e Problem Solutions Repository

Welcome to the "LeetCode Problem Solutions" repository! This repository contains a collection of LeetCode coding problems along with their corresponding solutions. Each problem is organized in its own folder, named after the problem title, and contains the problem statement, solution, and test cases.

Contents

Each problem folder follows the format problem-title, and within each folder, you will find:

• Problem Statement: A brief description of the problem from LeetCode, outlining the task to be solved.

• Solution: One or more well-commented solution files implemented in popular programming languages such as Python, Java, C++, or JavaScript. Each solution is accompanied by an explanation of the chosen approach and algorithm.

• Test Cases: Sample inputs and expected outputs are provided to validate the correctness of the solutions. These test cases cover both the problem's example inputs and additional edge cases.

Contribution

Contributions to this repository are highly encouraged! If you have a unique solution to an existing problem or want to add a new problem with its solution, please follow these steps:

1. Fork the repository to your GitHub account.

2. Create a new branch for your contribution.

3. Create a new folder with the problem title in a suitable category or the root directory if a suitable category doesn't exist.

4. Add the problem statement, solution file(s), and test cases in the problem folder.

5. Ensure that your code follows proper code formatting guidelines and is well-commented.

6. Create a pull request, describing the problem and solution you're adding or modifying.

7. Wait for the review and feedback from the repository maintainers.

Please note that all contributions will be subject to review to maintain the quality and integrity of the repository.

Stay Connected

• Star the repository to show your support and receive notifications about updates.

• Subscribe to notifications to stay up-to-date with new problem additions and other announcements.

• Connect with the community by participating in discussions, asking questions, and sharing insights.

Let's embark on this coding journey together, honing our problem-solving skills and mastering the art of algorithmic thinking. Happy coding!