jacobi, a Python code which uses the Jacobi iteration to solve a linear system with a symmetric positive definite (SPD) matrix.
The information on this web page is distributed under the MIT license.
jacobi is available in a C version and a C++ version and a Fortran90 version and a MATLAB version and an Octave version and a Python version and an R version.
cg, a Python code which implements a simple version of the conjugate gradient (CG) method for solving a system of linear equations of the form A*x=b, suitable for situations in which the matrix A is symmetric positive definite (SPD).
cg_rc, a Python code which implements the conjugate gradient method for solving a symmetric positive definite (SPD) sparse linear system A*x=b, using reverse communication.
gauss_seidel, a Python code which uses the Gauss-Seidel iteration to solve a linear system with a symmetric positive definite (SPD) matrix.
gauss_seidel_stochastic, a Python code which uses a stochastic version of the Gauss-Seidel iteration to solve a linear system with a symmetric positive definite (SPD) matrix.
test_matrix, a Python code which defines test matrices for which the condition number, determinant, eigenvalues, eigenvectors, inverse, null vectors, P*L*U factorization or linear system solution are known. Examples include the Fibonacci, Hilbert, Redheffer, Vandermonde, Wathen and Wilkinson matrices.
wathen_matrix, a Python code which compares storage schemes (full, banded, sparse triplet, sparse) and solution strategies (A\x, Linpack, conjugate gradient (CG)) for linear systems involving the Wathen matrix, which can arise when solving a problem using the finite element method (FEM).
