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A Python project for the Ciencias Naturales y Tecnología (CNYT) course at Escuela Colombiana de Ingeniería. Provides NumPy-based Jupyter notebooks solving workshops on complex linear algebra, from basic arithmetic to eigenvalues, Hermitian/unitary matrices, and quantum applications.

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NumPy Complex Linear Algebra Workshops

Educational project implementing complex linear algebra operations through interactive Jupyter notebooks that solve specific workshop assignments.

Overview

This project provides complete solutions to 4 workshops focusing on complex linear algebra using NumPy. Each notebook implements the exact requirements specified in the tasks/ folder, progressing from basic complex number operations to advanced quantum computing applications.

Workshop Solutions

Workshop 1: Complex Number Fundamentals

File: Taller1_ComplexIntro.ipynb
Tasks: tasks/Taller Espacios Vectoriales Complejos.pdf

Complete introduction to complex numbers including:

  • Cartesian and polar representation
  • Basic operations and complex arithmetic
  • Modulus and phase calculations
  • Complex conjugates and properties

Workshop 2: Vector and Matrix Operations

File: Taller2_Vector_Matrix_Operations.ipynb
Tasks: tasks/Taller Espacios Vectoriales Complejos 2.pdf

Advanced vector and matrix manipulations:

  • Complex column vector creation and operations
  • Matrix addition, scalar multiplication
  • Matrix-vector products and transformations
  • Complex matrix conjugates and transposes

Workshop 3: Vector Spaces and Eigenvalues

File: Taller3_Inner_Products_Eigenvalues.ipynb
Tasks: tasks/Taller Espacios Vectoriales Complejos 3.pdf

Inner product spaces and spectral analysis:

  • Complex inner products and norms
  • Eigenvalue and eigenvector computation
  • Distance and angle calculations in complex spaces
  • Orthogonality and normalization

Workshop 4: Advanced Matrix Theory

File: Taller4_Hermitian_Unitary_Tensor.ipynb
Tasks: tasks/Taller Espacios Vectoriales Complejos 4.pdf

Specialized matrices and quantum applications:

  • Hermitian matrix analysis and properties
  • Unitary matrices and transformations
  • Tensor products and composite systems
  • Quantum state evolution and measurements

Project Structure

numpy-complex-py/
├── Taller1_ComplexIntro.ipynb           # Workshop 1 solution
├── Taller2_Vector_Matrix_Operations.ipynb  # Workshop 2 solution
├── Taller3_Inner_Products_Eigenvalues.ipynb # Workshop 3 solution  
├── Taller4_Hermitian_Unitary_Tensor.ipynb  # Workshop 4 solution
├── tasks/                               # Original workshop assignments
│   ├── Taller Espacios Vectoriales Complejos.pdf
│   ├── Taller Espacios Vectoriales Complejos 2.pdf
│   ├── Taller Espacios Vectoriales Complejos 3.pdf
│   └── Taller Espacios Vectoriales Complejos 4.pdf
├── requirements.txt                     # Python dependencies
├── README.md                           # This file
└── LICENSE                            # GPL-3.0 License

Installation and Usage

  1. Clone the repository:

    git clone https://github.com/AnderssonProgramming/numpy-complex-py.git
    cd numpy-complex-py
  2. Install dependencies:

    pip install -r requirements.txt
  3. Launch Jupyter and open any workshop notebook:

    jupyter notebook

Requirements

  • Python 3.8+
  • NumPy 1.21+
  • Jupyter Notebook
  • Matplotlib (for visualizations)

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'feat: add some amazing feature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

Please use conventional commit messages (feat, fix, docs, test, chore, etc.).

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

License

GPL-3.0 License - see LICENSE file for details.

Acknowledgments

Complex Number Operations

  • Complex arithmetic and properties
  • Cartesian ↔ Polar conversions
  • Modulus and argument calculations

Vector Spaces

  • Complex vector operations
  • Inner products and norms
  • Orthogonality and projections

Matrix Theory

  • Complex matrix operations
  • Eigenvalue decomposition
  • Hermitian and unitary matrices
  • Tensor products

Quantum Applications

  • State vector representation
  • Unitary evolution
  • Measurement operators
  • Composite quantum systems

About

A Python project for the Ciencias Naturales y Tecnología (CNYT) course at Escuela Colombiana de Ingeniería. Provides NumPy-based Jupyter notebooks solving workshops on complex linear algebra, from basic arithmetic to eigenvalues, Hermitian/unitary matrices, and quantum applications.

Topics

Resources

Stars

2 stars

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0 watching

Forks

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