Numerical Linear Algebra Toolkit
Purpose
Provides high-performance numerical linear algebra operations for scientific computing and mathematical analysis.
Capabilities
- Matrix decompositions (LU, QR, SVD, Cholesky, Schur)
- Eigenvalue/eigenvector computation
- Sparse matrix operations
- Iterative solvers (CG, GMRES, BiCGSTAB)
- Condition number estimation
- Error analysis and bounds
Usage Guidelines
- Decomposition Selection: Choose appropriate factorization for the problem
- Sparsity Exploitation: Use sparse formats for large sparse matrices
- Iterative Methods: Apply iterative solvers for very large systems
- Conditioning: Assess and monitor condition numbers
Tools/Libraries
- LAPACK
- BLAS
- SuiteSparse
- Eigen