gauss_seidel


gauss_seidel, a Python code which uses the Gauss-Seidel iteration to solve a linear system with a symmetric positive definite (SPD) matrix.

The main interest of this code is that it is an understandable analogue to the stochastic gradient descent method used for optimization in various machine learning applications.

Licensing:

The computer code and data files described and made available on this web page are distributed under the MIT license

Languages:

gauss_seidel is available in a MATLAB version and a Python version and an R version.

Related Data and Programs:

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_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 symmetric positive definite (SPD) linear systems.

test_mat, a Python code which defines test matrices.

Reference:

Source Code:


Last modified on 08 February 2022.