A comprehensive collection of 81+ algorithm problems and solutions designed for the ByteDance MarsCode Youth Training Camp.
Features detailed explanations, optimized Python solutions, and real-world application scenarios.
Perfect for algorithm learning, interview preparation, and competitive programming.
🎯 Start Practicing · 📚 Problem Categories · 📖 Documentation · 🤝 Contributing
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⚡ Accelerating algorithm mastery through structured practice and comprehensive solutions.
[!TIP] This repository contains meticulously crafted algorithm problems with detailed explanations and optimized solutions.
Comprehensive coverage of algorithm fundamentals and advanced topics
String Processing, Dynamic Programming, and Array Manipulation Problems
Algorithm Categories Covered:
Important
This repository is designed for the ByteDance MarsCode Youth Training Camp and serves as a comprehensive resource for algorithm learning and interview preparation. It features 81+ carefully selected problems covering fundamental to advanced algorithm concepts.
📑 Table of Contents
- 🚀 ByteDance MarsCode Algorithm PracticeComprehensive Algorithm Training Collection for MarsCode Youth Camp
Welcome to the ByteDance MarsCode Algorithm Practice repository! This comprehensive collection is specifically designed for participants of the ByteDance MarsCode Youth Training Camp, offering a structured approach to algorithm learning and problem-solving.
Whether you're preparing for technical interviews, participating in competitive programming, or simply looking to strengthen your algorithmic thinking, this repository provides carefully curated problems with detailed explanations and optimized solutions.
Note
- Python 3.6+ recommended for running solutions
- Each problem includes detailed problem statement, solution approach, and complexity analysis
- Problems are organized by difficulty and topic for systematic learning
- All solutions are tested and optimized for performance
| 📚 Learn | 💡 Practice | 🎯 Master |
|---|---|---|
| Comprehensive explanations | 81+ diverse problems | Interview preparation |
| Step-by-step solutions | Real-world scenarios | Competitive programming |
Tip
⭐ Star this repository to bookmark your algorithm learning journey and stay updated with new problems!
- 81+ Algorithm Problems: Carefully selected from basic to advanced levels
- Detailed Explanations: Each problem includes comprehensive problem analysis
- Optimized Solutions: Python implementations with optimal time and space complexity
- Real-world Scenarios: Problems based on practical applications and interview questions
- Step-by-step Approach: Clear problem-solving methodology
- Complexity Analysis: Time and space complexity for each solution
- Multiple Approaches: Different solution strategies where applicable
- Best Practices: Clean, readable, and maintainable code
- Organized Structure: Problems categorized by topics and difficulty
- Easy Navigation: Consistent naming and folder structure
- Quick Reference: Problem titles and categories clearly labeled
- Testing Support: Example inputs and expected outputs provided
Our algorithm collection covers essential computer science topics:
- 🔢 Array Manipulation: Finding unique elements, subarray problems
- 🔗 String Processing: Pattern matching, transformation, encoding/decoding
- 📊 Mathematical Problems: Number theory, combinatorics, statistical analysis
- 🎯 Dynamic Programming: Optimization problems, sequence analysis
- 🌳 Graph Theory: Path finding, traversal algorithms
- 🔍 Search & Sort: Binary search, custom sorting algorithms
- 🎮 Game Theory: Strategic decision making, probability
- 🧬 Bioinformatics: DNA sequence analysis, string distance
- 💰 Financial Modeling: Stock trading, optimization problems
- 🎲 Gaming Logic: Card games, board games, strategy problems
- 📈 Data Analysis: Statistical calculations, ranking algorithms
Balanced distribution from beginner to advanced levels
📁 juejin-algorithm-practice/
├── 📂 juejin1-10/ # Problems 1-10 (Basics)
│ ├── 📄 juejin1.md # Problem description
│ ├── 🐍 juejin1.py # Python solution
│ └── ...
├── 📂 juejin11-20/ # Problems 11-20 (Intermediate)
├── 📂 juejin21-30/ # Problems 21-30 (String & Array)
├── 📂 juejin31-40/ # Problems 31-40 (Dynamic Programming)
├── 📂 juejin41-50/ # Problems 41-50 (Advanced Algorithms)
├── 📂 juejin51-60/ # Problems 51-60 (Mathematical)
├── 📂 juejin61-70/ # Problems 61-70 (Graph & Tree)
├── 📂 juejin71-80/ # Problems 71-80 (Optimization)
├── 📂 juejin81-90/ # Problems 81-90 (Expert Level)
├── 📄 README.md # This documentation
└── 📄 LICENSE # MIT License
Each problem follows a consistent structure:
juejinX.md: Detailed problem description, examples, and analysisjuejinX.py: Clean, optimized Python solution with comments
| Metric | Value | Description |
|---|---|---|
| 🎯 Total Problems | 81+ | Comprehensive problem collection |
| 🐍 Python Solutions | 81+ | Optimized implementations |
| 📚 Categories | 10+ | Diverse algorithm topics |
| ⏱️ Avg. Solve Time | 15-45 min | Per problem (varies by difficulty) |
| 🎓 Skill Level | Beginner to Expert | Progressive difficulty curve |
Problem Difficulty Distribution:
- 🟢 Beginner (1-20): 25% - Foundation building
- 🟡 Intermediate (21-50): 40% - Core concepts
- 🟠 Advanced (51-70): 25% - Complex algorithms
- 🔴 Expert (71-81): 10% - Interview-level challenges
Important
Ensure you have Python installed on your system:
- Python 3.6+ (Download)
- Text Editor (VS Code, PyCharm, or any preferred IDE)
- Git for cloning the repository (Download)
1. Clone the Repository
git clone https://github.com/ChanMeng666/juejin-algorithm-practice.git
cd juejin-algorithm-practice2. Explore Problems
# Navigate to any problem set
cd juejin1-10
# Read a problem description
cat juejin1.md
# Run the solution
python juejin1.py3. Start Learning
Choose your learning path:
- Beginner: Start with
juejin1-10/ - Intermediate: Jump to
juejin21-30/ - Advanced: Challenge yourself with
juejin51-60/
Each Python file can be executed directly:
# Run individual solutions
python juejin1.py
python juejin25.py
python juejin55.py
# Expected output: Test results and verificationEach problem file (.md) contains:
- Problem Statement: Clear description of the challenge
- Input/Output Examples: Sample test cases
- Constraints: Input limitations and requirements
- Solution Approach: Step-by-step methodology
- Complexity Analysis: Time and space complexity
- Code Implementation: Detailed solution walkthrough
Each Python solution (.py) includes:
def solution(input_params):
"""
Problem-specific solution with optimized approach
Args:
input_params: Problem input parameters
Returns:
Expected output based on problem requirements
Time Complexity: O(n) or as specified
Space Complexity: O(1) or as specified
"""
# Implementation here
pass
# Test cases
if __name__ == "__main__":
# Verification with sample inputs
assert solution(test_input) == expected_output- Read & Understand: Study the problem statement thoroughly
- Plan Your Approach: Think through the solution before coding
- Implement: Write your solution in Python
- Compare: Check against the provided optimized solution
- Analyze: Understand time/space complexity trade-offs
Start here if you're new to algorithms:
- Basic array operations
- Simple string manipulations
- Fundamental mathematical problems
- Introduction to algorithmic thinking
Build core algorithm skills:
- Dynamic programming basics
- String algorithms
- Number theory
- Problem-solving patterns
Master complex algorithms:
- Advanced dynamic programming
- Graph algorithms
- Optimization problems
- Real-world applications
Challenge yourself with:
- Complex system design problems
- Multi-step optimization
- Interview-level challenges
- Competitive programming style
Environment Setup:
# Clone repository
git clone https://github.com/ChanMeng666/juejin-algorithm-practice.git
cd juejin-algorithm-practice
# Create virtual environment (optional)
python -m venv venv
source venv/bin/activate # Linux/Mac
# or
venv\Scripts\activate # Windows
# Verify Python version
python --versionTo contribute a new problem:
- Create problem description file:
juejinX.md - Implement solution file:
juejinX.py - Follow the existing format and structure
- Include comprehensive test cases
- Add complexity analysis
Problem Template:
# Problem Title
## Problem Description
[Detailed problem statement]
## Examples
[Input/output examples]
## Solution Approach
[Step-by-step explanation]
## Code Implementation
[Python solution]
## Complexity Analysis
- Time Complexity: O(...)
- Space Complexity: O(...)# Run individual problem
python juejinX.py
# Verify all solutions in a directory
for file in juejin1-10/*.py; do
echo "Testing $file"
python "$file"
doneWe welcome contributions to improve this algorithm collection! Here's how you can help:
1. Fork & Clone
git clone https://github.com/your-username/juejin-algorithm-practice.git
cd juejin-algorithm-practice2. Create a Branch
git checkout -b feature/new-problem-solution3. Make Your Changes
- Add new problems with solutions
- Improve existing explanations
- Fix bugs or optimize solutions
- Enhance documentation
4. Submit a Pull Request
- Provide clear description of changes
- Include test cases for new problems
- Ensure code follows Python best practices
Code Quality:
- Follow PEP 8 Python style guidelines
- Include comprehensive docstrings
- Add meaningful comments for complex logic
- Ensure optimal time/space complexity
Documentation:
- Clear and concise problem descriptions
- Include multiple examples
- Provide step-by-step solution explanation
- Add complexity analysis
Problem Requirements:
- Original or educationally valuable problems
- Multiple test cases provided
- Solution verified and optimized
- Appropriate difficulty classification
This project is licensed under the MIT License - see the LICENSE file for details.
You are free to:
- ✅ Use for commercial purposes
- ✅ Modify and distribute
- ✅ Use in private projects
- ✅ Use for educational purposes
Chan Meng 🚀 Creator & Maintainer 💻 Algorithm Enthusiast |
Connect with the Author:
- 🔗 GitHub: ChanMeng666
- 💼 LinkedIn: chanmeng666
- 📧 Email: [email protected]
- 🌐 Portfolio: chanmeng.live
🎯 About ByteDance MarsCode Youth Training Camp
The MarsCode Youth Training Camp is a comprehensive technical training program by ByteDance, designed to develop practical programming skills and algorithmic thinking. This repository serves as a supplementary resource for camp participants and the broader programming community.
🚀 Key Training Objectives:
- Master fundamental algorithms and data structures
- Develop problem-solving methodologies
- Prepare for technical interviews
- Build confidence in competitive programming
📚 Related Resources:
- ByteDance Technical Blog
- Algorithm Visualization Tools
- Python Documentation
- LeetCode Practice Platform
⭐ Star this repository if it helps your algorithm learning journey!
📖 Share with friends who are also learning algorithms and data structures!
🤝 Contribute to help others in their programming journey!
🌟 Building tomorrow's tech leaders through structured algorithm practice 🌟
