Optimization Methods for Engineering Applications (OMEA)

A comprehensive collection of optimization algorithms implemented in Python for engineering applications, including linear, nonlinear, and constrained optimization methods with practical examples.

📋 Repository Contents

Implemented Methods:

1. Unrestricted Search Method - View Implementation
2. Dichotomous Search Method - View Implementation
3. Internal Halving Method - View Implementation
4. Golden Section Method - View Implementation
5. Fibonacci Method - View Implementation
6. Simplex Method(Dual & normal simplex) - View Implementation
7. Two Phase Simplex Method - View Implementation
8. Exhaustive Search Method - View Implementation
9. Newton Method for Multivariable - View Implementation
10. Steepest Descent(Cauchy) Method - View Implementation
11. Conjugate Gradient Method - View Implementation
12. Newton Raphson Method - View Implementation
13. Quasi Newton Method - View Implementation
14. Secant Method - View Implementation
15. Binary Genetic Algorithm -View Implementation

📊 Current Status

• 🔄 Additional methods: Coming soon

🤝 Contributing

Feel free to contribute additional optimization methods, improvements to existing implementations, or enhanced documentation.

📄 License

This project is available for educational and research purposes.

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