Small Matrix Eigenvalue Computation

About

Provides expert guidance and implementation patterns for efficiently calculating the largest eigenvalue of small dense matrices, specifically targeting dimensions between 2 and 10. By bypassing standard library overhead and utilizing direct LAPACK calls via Cython, this skill helps developers achieve significant performance gains in numerical linear algebra tasks where standard NumPy or SciPy wrappers become the bottleneck. It includes a comprehensive decision tree for selecting optimization strategies based on matrix size and properties, along with detailed verification protocols to ensure mathematical accuracy and performance improvements.

Key Features

• Direct LAPACK integration via Cython to minimize Python wrapper overhead
• Robust mathematical and performance verification strategies
• Matrix size-specific optimization strategies for dense and sparse data
• Implementation checklists for Cython LAPACK bindings and work array allocation
• Guidance for handling complex dominant eigenvalues and rotation matrices
• 16 GitHub stars

Use Cases

• Optimizing high-frequency numerical linear algebra routines in performance-critical applications
• Implementing low-latency matrix operations that bypass standard NumPy overhead
• Developing specialized scientific computing modules requiring direct LAPACK access