LinearAlgebra-WS is a transparent, minimal, and open-source linear algebra library built with Rust, by Willy Sajbeni.
Ideal for students, researchers, engineers, and developers who value speed, privacy, and control.
cargo install linear_algebra_ws
linear_algebra_wsCurrent Features
Vector Operations
Sum
Subtraction
Multiplication (element-wise)
Division (element-wise)
User-friendly input system
Easily input vectors and matrices from the terminal
Upcoming Features (Roadmap)
Matrix Operations
Sum of Matrices
Subtraction of Matrices
Multiplication of Matrices
Matrix Inversion
Matrix Division (via Multiplication by Inverse)
LU Decomposition
Eigenvalues and Eigenvectors
Advanced Numerical Methods
Iterative Methods for Linear Systems (Jacobi, Gauss-Seidel)
Direct Methods (Gaussian Elimination, LU Factorization)
Conjugate Gradient Method (for sparse systems)
Gradient Optimizers
Simple Gradient Descent
Stochastic Gradient Descent (SGD)
Adam Optimizer
RMSProp
Dimensionality Reduction
PCA (Principal Component Analysis)
SVD (Singular Value Decomposition)
LLE (Locally Linear Embedding)
t-SNE (t-Distributed Stochastic Neighbor Embedding)
Philosophy
100% Pure Rust
Zero telemetry, zero tracking
Educational and extendable
Mathematical clarity prioritized over raw performance
Fully Open Source (MIT License)
Why?
Most modern machine learning libraries (TensorFlow, PyTorch, etc.) embed telemetry and hidden behaviors.
LinearAlgebra-WS is different — it's completely open, simple, transparent, and under your full control. No tracking. No hidden code. Just math. License
This project is licensed under the MIT License.
Built and maintained by Willy Sajbeni.
Website: www.sajbeni.com
Email: willy@sajbeni.com
GitHub: github.com/willySajbeni
Future Enhancements
Unit tests (#[cfg(test)])
Documentation examples
CLI enhancements
Support for real-world numerical problems
Summary
LinearAlgebra-WS is your go-to open-source toolbox to build serious numerical computation engines in Rust — with full mathematical transparency and no compromises.