Basic linear algebra routines and classes optimized for ease of use
- Matrices and Vectors
- Sparse Matrices and Vectors
- Get a basis for a vector space via QR (Gram-Schmidt, Householder, Givens, Randomized method)
- Compute matrix decomposition (LU, ILU, SVD, Cholesky, QR)
- Perform eigenvalue decomposition (Lanczos, Arnoldi, QR Algorithm)
- Solve linear systems:
- Direct Methods: Forward/Back substitution, Diagonal solve, Tridiagonal solve, Unitary solve
- Iterative Methods: Jacobi, Gauss-Seidel, SOR, Steepest Descent, CG, CR, BiCG, BiCR, BiCGSTAB, GMRES(k), AMG
- Preconditioners: Jacobi, Gauss-Seidel, Symmetric Gauss-Seidel, SOR, SSOR, Incomplete Cholesky, Incomplete LU, AMG