cg_ne, a Python code which implements the conjugate gradient method (CG) for the normal equations, that is, a method for solving a system of linear equations of the form A*x=b, where the matrix A is not symmetric positive definite (SPD). In this case, it is attempted to set up and solve the normal equations A'*A*x=A'*b.
The information on this web page is distributed under the MIT license.
cg_ne is available in a MATLAB version and an Octave version and a Python 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 where the matrix A is symmetric positive definite (SPD) (only real, positive eigenvalues).
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 implements the Gauss-Seidel iteration for linear systems.
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.
jacobi, a Python code which implements the Jacobi iteration for linear systems.