This repository serves as a detailed guide to solving a variety of LeetCode problems. It aims to provide an exhaustive understanding of each problem and its potential solutions by showcasing diverse problem-solving methods.
The primary objective of this repository is educational. The methods and solutions featured here are for illustrative purposes, demonstrating the fundamental concepts of algorithmic problem-solving. As such, these solutions may not adhere to production-level code quality standards. They are not designed to handle all possible edge cases, nor do they ensure optimal error handling and recovery. The solutions are tailored to answer LeetCode problems specifically, and they may not be sufficiently generic for direct use in real-world cases. Users are encouraged to view these solutions as a stepping stone to understanding the underlying principles and apply these learnings to create robust, production-ready solutions for their own use cases.
Each problem is solved using several distinct methods, each accompanied by a clear and concise explanation. These explanations guide users through the underlying logic and workings of the approach, ensuring a deep understanding of the problem-solving process.
The solutions are organised by problem ID, which is the number assigned to each problem by LeetCode. The problem ID is used as the name of the directory containing the solutions for that problem. Each problem directory contains a README file that provides a brief overview of the problem and its solutions. It also contains a table that summarises the time and space complexities of each solution method.
Each solution method is paired with a theoretical analysis of its time and space complexity, expressed in Big O notation. This analysis provides a theoretical estimate of the solution's efficiency, allowing users to compare the complexities of different methods and gain a better understanding of the trade-offs involved in selecting a particular approach.
The solutions featured in this repository are not intended to be the most efficient or optimal solutions to the problems. Instead, they are designed to be easy to understand and implement, making them ideal for educational purposes.
Also remember, the complexity analysis provided is theoretical and does not account for the constant factors involved in the implementation. As such, the analysis should be used as a guideline for comparing the relative efficiencies of different methods, rather than as a definitive measure of the solution's performance. The actual running time of an algorithm may be affected by various factors, including the size and nature of the input, code optimisations, and the execution environment.
Collective efforts from the community can greatly enhance this resource, and therefore contributions are more than welcome! To contribute, please refer to the guidelines in the contributing guidelines.
Distributed under the MIT License. See LICENSE for more information.
Should there be any queries, suggestions, or bug reports, please feel free to raise an issue or submit a pull request.