AlgoGym - Personal Algorithm Training Ground

Project Overview

AlgoGym is a self-developed algorithm training project designed to systematically enhance algorithm design and problem-solving skills. The project integrates a testing framework that supports rapid implementation, validation, and optimization of solutions to various algorithmic challenges.

Key Features

• Structured Practice: Exercises organized by algorithm categories (strings, arrays, graph theory, dynamic programming, etc.)
• Immediate Feedback: Quick validation of solution correctness through the integrated testing framework
• Performance Analysis: Basic analysis of time and space complexity for implemented algorithms
• Progressive Learning: Difficulty gradient from fundamental to advanced problems
• Code Quality Assurance: Integration of code formatting and static analysis tools

Technology Stack

• C++17/20 core language implementation
• GoogleTest testing framework
• CMake build system
• clang-format for code style consistency
• Git version control
• Project Structure

AlgoGym/
├── include/                                   # Public headers
├── src/                                       # Main source code
├── tests/                                     # Test cases
│   ├── twoPointers/                           # Two pointers algorithm tests
│   │      └──isPalindrome                     # Problem name
│   │               ├──isPalindrome.md         # Problem description
│   │               └──isPalindrome.cpp        # Problem solution and tests
│   ├── dynamicProgramming/                    # Dynamic programming tests
│   └── ...
├── tools/                                     # Helper scripts
├── .clang-format                              # Code formatting configuration
├── CMakeLists.txt                             # CMake configuration
└── README.md                                  # Project documentation

Usage

1. Adding a New Problem: Create a new test file in the appropriate category under tests/
2. Implementing Solutions: Develop algorithm logic according to test requirements
3. Running Tests:

mkdir -p build && cd build
cmake ..
make
./Yocto_AlgoGym list                           # List all the available tests
./Yocto_AlgoGym isPalindrome                   # Run teses of problem isPalindrome

4. Optimizing Solutions: Improve algorithms based on test results and performance analysis

Learning Value

• Systematic mastery of classic algorithms and data structures
• Development of complex problem-solving mindset
• Enhancement of code design and implementation skills
• Preparation for technical interviews
• Building a personal knowledge base of algorithmic solutions

Future Plans

• Add automatic algorithm complexity analysis
• Integrate performance benchmarking
• Implement algorithm visualization components
• Expand into more advanced algorithm domains (e.g., basic machine learning algorithms)
• Develop automatic synchronization with LeetCode/HackerRank problem libraries

This project is not just a practice tool but also a record of my algorithmic growth. Through continuous accumulation and practice, it will become an important milestone in my personal technical capabilities.