Add docs

README.md

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 # vonMises
-von Mises Iteration
+
+vonMises is a numerical algorithm for solving eigenvalue problems using the deflation method and power iteration. It is implemented in C++ and wrapped for Python using [scikit-build](https://scikit-build.readthedocs.io/) (skbuild) to provide a high-performance solution for eigenvalue computations in Python environments.
+
+## Features
+
+- Solves eigenvalue problems using **Von Mises iteration** and **Rayleigh Quotient** methods.
+- Supports deflation for computing multiple eigenvalues and eigenvectors.
+- High-performance implementation using **Eigen3** and **OpenMP**.
+- Extensive logging and debugging support via the **fmt** library.
+
+## Installation
+
+You can install vonMises via pip:
+
+```bash
+pip install vonMises
+```
+
+Make sure you have the required dependencies in your environment, especially **Eigen3** and **fmt** libraries.
+
+## Building from Source
+
+To build vonMises from source, ensure you have **CMake**, **Eigen3**, and **fmt** installed. The project uses **scikit-build** to handle the Python build and installation process. You can clone the repository and build the project as follows:
+
+```bash
+git clone https://github.com/your-username/vonMises.git
+cd vonMises
+pip install .
+```
+
+## License
+
+vonMises is licensed under the MIT License. See [LICENSE](LICENSE) for details.
+
+## Contributing
+
+Contributions are welcome! Feel free to submit issues or pull requests.

docs/docs/index.md

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+# vonMises
+
+The **vonMises** solves the eigenvalue problem using a combination of the Power Iteration 
+method and Rayleigh Quotient to compute both the dominant eigenvalue and its associated eigenvector. 
+The algorithm uses *deflation* to remove previously computed eigenvalues from the matrix.
+
+## Power Iteration Method
+
+The Power Iteration method is used to compute the largest eigenvalue of a matrix \( A \) and its corresponding 
+eigenvector. The steps are as follows:
+
+1. Initialize with a random vector \( x_0 \).
+2. Normalize \( x_0 \) to have a unit norm.
+3. Iteratively compute \( x_{k+1} = A x_k \) and normalize the resulting vector.
+
+The process converges when \( x_k \) stabilizes. The largest eigenvalue \( \lambda \) is computed by:
+
+\[
+\lambda = \frac{x_k^T A x_k}{x_k^T x_k}
+\]
+
+## Rayleigh Quotient
+
+The Rayleigh Quotient is defined as:
+
+\[
+R(x) = \frac{x^T A x}{x^T x}
+\]
+
+This method refines the computed eigenvalue after performing the Power Iteration.
+
+## Complete Algorithm
+
+The vonMises algorithm combines Power Iteration with Rayleigh Quotient and deflation:
+
+```python
+Algorithm vonMises(A):
+    for a in range(n):
+        x = power_iteration(A)
+        λ = rayleigh_quotient(A, x)
+        A = A - λ * (x @ x.T)
+```
+
+The algorithm repeats until all eigenvalues and eigenvectors are computed.
+
+## Deflation
+
+After finding each eigenvalue and eigenvector pair \( (\lambda, x) \), the matrix \( A \) is deflated by subtracting the rank-1 update:
+
+\[
+A = A - \lambda \cdot (x \cdot x^T)
+\]
+
+This ensures that subsequent iterations of the algorithm find the next largest eigenvalue of the matrix.
+
+## Example
+
+Given a matrix \( A \), the vonMises algorithm computes its eigenvalues and eigenvectors as follows:
+
+1. Power Iteration is applied to find the largest eigenvalue \( \lambda_1 \) and its corresponding eigenvector \( x_1 \).
+2. The matrix is deflated: \( A = A - \lambda_1 \cdot (x_1 \cdot x_1^T) \).
+3. The process is repeated for the remaining matrix to compute the next largest eigenvalue and eigenvector.

docs/mkdocs.yml

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+site_name: vonMises
+site_url: https://saudzahirr.github.io/vonMises
+theme:
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+  features:
+    - navigation.tabs
+    - navigation.sections
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+
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