JavaCleanCode
Implementation of Java's most famous algorithms, including sorting (QuickSort, MergeSort), searching (Binary Search), and graph traversal (DFS, BFS). Each algorithm is optimized for performance, explained with examples, and designed to showcase core Java capabilities in solving computational problems.
Install / Use
/learn @jerry-felipe/JavaCleanCodeREADME
Java Famous Algorithms Collection
This repository contains implementations of some of the most famous and widely-used algorithms in Java. Each algorithm is designed to demonstrate efficiency, clarity, and practical application, complete with examples and explanations.
Algorithms Included
Sorting Algorithms
- QuickSort: A divide-and-conquer algorithm that efficiently sorts data by partitioning.
- MergeSort: A stable, recursive sorting algorithm ideal for large datasets.
- BubbleSort: A simple sorting algorithm for small datasets.
Searching Algorithms
- Binary Search: Fast searching in sorted arrays with O(log n) complexity.
- Linear Search: A straightforward search method for unsorted datasets.
Graph Traversal Algorithms
- Depth-First Search (DFS): Explores as far as possible along a branch before backtracking.
- Breadth-First Search (BFS): Explores all neighbors at the present depth before moving deeper.
Features
- 🛠 Well-documented code: Clear comments and step-by-step explanations.
- 🚀 Optimized implementations: Focused on performance and readability.
- 📚 Usage examples: Real-world scenarios and test cases for each algorithm.
Getting Started
Prerequisites
- Java Development Kit (JDK 17 or higher)
- A code editor or IDE (e.g., IntelliJ IDEA, Eclipse)
Setup
-
Clone the repository: '''bash git clone https://github.com/jerry-felipe/java-famous-algorithms.git cd java-famous-algorithms '''
-
Open the project in your favorite IDE.
-
Run the desired algorithm:
- Navigate to the 'src/' folder.
- Execute the example files to see the algorithm in action.
Usage
- Explore the 'src/' folder for individual algorithm implementations.
- Modify input arrays or graphs in the example files to test with custom data.
- Run the examples: '''bash javac src/QuickSortExample.java java src/QuickSortExample '''
Contributing
Contributions are welcome!
To contribute:
- Fork the repository.
- Create a feature branch:
'''bash git checkout -b feature/new-algorithm ''' - Commit your changes:
'''bash git commit -m "Add a new algorithm implementation" ''' - Push the branch:
'''bash git push origin feature/new-algorithm ''' - Open a pull request.
License
This project is licensed under the MIT License. See the 'LICENSE' file for details.
Contact
For questions or feedback, feel free to reach out:
- GitHub Issues: https://github.com/jerry-felipe/java-famous-algorithms/issues
- Email: jerry.felipe@gmail.com
Related Skills
claude-opus-4-5-migration
111.3kMigrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5
model-usage
352.5kUse CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
TrendRadar
51.2k⭐AI-driven public opinion & trend monitor with multi-platform aggregation, RSS, and smart alerts.🎯 告别信息过载,你的 AI 舆情监控助手与热点筛选工具!聚合多平台热点 + RSS 订阅,支持关键词精准筛选。AI 智能筛选新闻 + AI 翻译 + AI 分析简报直推手机,也支持接入 MCP 架构,赋能 AI 自然语言对话分析、情感洞察与趋势预测等。支持 Docker ,数据本地/云端自持。集成微信/飞书/钉钉/Telegram/邮件/ntfy/bark/slack 等渠道智能推送。
mcp-for-beginners
15.8kThis open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
