eigenfind is a lightweight Python library that allows you to compute eigenvectors from known eigenvalues of a square matrix — a task commonly needed in theoretical mathematics, linear algebra education, and symbolic or numerical analysis.
While most libraries like NumPy and SciPy compute eigenvalues and eigenvectors together, eigenfind fills a specific niche: solving the eigenvalue problem in reverse — finding eigenvectors when you already know one or more eigenvalues.
This is achieved by solving the homogeneous linear system:
…which defines the eigenspace for a given eigenvalue λ of matrix A.
- 🔍 Find eigenvectors corresponding to a given eigenvalue
- 📐 Works with both numeric (NumPy/SciPy) and symbolic (SymPy) matrices
- 📚 Educational use: ideal for students, educators, and math enthusiasts
- 🧠 Supports defective matrices (partial functionality)
- 🧪 Easy to test and integrate into other math tools
- Teaching or learning linear algebra
- Verifying results from numerical solvers
- Debugging or inspecting eigenvalue computations
- Symbolic math derivations
- Building introspection tools for PCA or matrix decompositions