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Contributing Guide

Thank you for your interest in contributing! This repository is primarily my personal algorithm learning notebook, and the solutions here reflect my own problem-solving process. You’re welcome to contribute in specific ways, as outlined below.

1. New Problem Contributions

  • This repo is mainly for my own solutions — either ones I came up with myself, or ones I learned from others and re-implemented after fully understanding them.
  • I’m not accepting new problems or solutions directly written by others.
  • That said, PRs for bug fixes or improvements to existing solutions (e.g., cleaner, more efficient, or easier to understand) are always welcome! 🙏

2. Supported Languages

  • Each solution is implemented in both Python and C++, using the same algorithms and data structures, to demonstrate that the solution performs well in two languages with very different characteristics:
    • C++: compiled, high-performance, and memory-efficient
    • Python: interpreted, concise, easy to prototype, emphasizing clarity and readability
  • This approach helps illustrate that the algorithm itself is optimal and robust, independent of language-specific performance.
  • Both languages are widely used in competitive programming, coding interviews, and real-world applications.
  • Python2 is used for consistency with earlier solutions. Separate Python3 versions are not provided, as the syntax differences are minor and conversion is straightforward.
  • PRs improving existing solutions in either language are welcome.

3. Folder Structure and Naming

  • Please follow the existing folder structure and naming conventions when submitting PRs — it helps keep the repo organized and consistent.
  • Each solution should be placed according to the current folder structure.
  • Update the solution file with relevant information, including time and space complexity analysis.

4. Coding Style

  • Solutions adopt a competitive programming style emphasizing coding patterns:
    • Names are short and concise, using generic algorithm/data structure terms (dp, heap, graph, node) while avoiding problem-specific or business terms.
    • Focus is on algorithmic techniques and logic, not problem-specific context.
    • Common coding patterns, algorithms, and templates are clearly separated from problem-specific logic, making it easy to see:
      • What to modify for a new problem
      • What to reuse as a template or pattern
    • This approach keeps code readable, maintainable, and efficient to implement, while also being highly performant and memory-efficient, helping learners quickly recognize reusable patterns versus problem-specific adjustments.

Warning

This is not a production-level or FAANG-standard style. It’s intended for personal learning and competitive programming practice. Please keep this in mind if referencing the code for interviews or projects.

5. Testing & Validation

  • Each solution must pass all test cases on LeetCode (or the original problem source) to ensure correctness.
  • Writing additional unit tests is not required, and is optional for personal verification only.

6. Acknowledgement

Thanks again for understanding the purpose of this repo! Your support really means a lot! 🙏