#LnormInf corresponds to the absolute value of the greatest element of the vector. X = (b - np.dot(A, x) - np.dot(A, x_old)) / A The Jacobi method is a matrix iterative method used to solve the equation A x b for a known square matrix A of size n × n and known vector b or length n. If the function returns None, the minimization is aborted. Return the function value and set approxgradTrue. Return the function value but supply gradient function separately as fprime.
Print ('The solution vector in iteration', iter1, 'is:', x) This program implements Gauss Seidel Iteration Method for solving systems of linear equation in python programming language. Must do one of: Return f and g, where f is the value of the function and g its gradient (a list of floats). Must do one of: Return f and g, where f is the value of the function and g its gradient (a list of floats).
def gauss_seidel(A, b, tolerance, max_iterations, x): The block GaussSeidel procedure is widely used for the resolution of the strong coupling in the computer simulation of fluidstructure interaction. Instead I created my own little function that with the help of a permutation matrix as seen in another answer of mine permutation matrix will produce the solution (x vector) for any square matrix, including those with zeros on the diagonal. I know this is old but, I haven't found any pre existing library in python for gauss - seidel.
