Jacobi method


In numerical linear algebra, the Jacobi method is an iterative algorithm for determining the solutions of a strictly diagonally dominant system of linear equations. Each diagonal element is solved for, and an approximate value is plugged in. The process is then iterated until it converges. This algorithm is a stripped-down version of the Jacobi transformation method of matrix diagonalization. The method is named after Carl Gustav Jacob Jacobi.

Description

Let be a square system of n linear equations, where:
When and are known, and is unknown, we can use the Jacobi method to approximate. The vector denotes our initial guess for . We denote as the k-th approximation or iteration of, and is the next iteration of.

Matrix-based formula

Then A can be decomposed into a diagonal component D, a lower triangular part L and an upper triangular part U:The solution is then obtained iteratively via

Element-based formula

The element-based formula for each row is thus:The computation of requires each element in except itself. Unlike the Gauss–Seidel method, we cannot overwrite with, as that value will be needed by the rest of the computation. The minimum amount of storage is two vectors of size n.

Algorithm

Input:, matrix A, right-hand side vector b, convergence criterion
Output:
Comments: pseudocode based on the element-based formula above
while convergence not reached do
for i := 1 step until n do

for j := 1 step until n do
if j ≠ i then

end
end

end
increment k
'''end'''

Convergence

The standard convergence condition is when the spectral radius of the iteration matrix is less than 1:
A sufficient condition for the method to converge is that the matrix A is strictly or irreducibly diagonally dominant. Strict row diagonal dominance means that for each row, the absolute value of the diagonal term is greater than the sum of absolute values of other terms:
The Jacobi method sometimes converges even if these conditions are not satisfied.
Note that the Jacobi method does not converge for every symmetric positive-definite matrix. For example,

Examples

Example question

A linear system of the form with initial estimate is given by
We use the equation, described above, to estimate. First, we rewrite the equation in a more convenient form, where and. From the known values
we determine as
Further, is found as
With and calculated, we estimate as :
The next iteration yields
This process is repeated until convergence. The solution after 25 iterations is

Example question 2

Suppose we are given the following linear system:
If we choose as the initial approximation, then the first approximate solution is given by
Using the approximations obtained, the iterative procedure is repeated until the desired accuracy has been reached. The following are the approximated solutions after five iterations.
0.62.27272-1.11.875
1.047271.7159-0.805220.88522
0.932632.05330-1.04931.13088
1.015191.95369-0.96810.97384
0.988992.0114-1.01021.02135

The exact solution of the system is.

Python example


import numpy as np
ITERATION_LIMIT = 1000
  1. initialize the matrix
A = np.array
  1. initialize the RHS vector
b = np.array
  1. prints the system
print
for i in range:
row =
print
print
x = np.zeros_like
for it_count in range:
if it_count != 0:
print
x_new = np.zeros_like
for i in range:
s1 = np.dot
s2 = np.dot
x_new = / A
if x_new x_new:
break
if np.allclose:
break
x = x_new
print
print
error = np.dot - b
print
print

Weighted Jacobi method

The weighted Jacobi iteration uses a parameter to compute the iteration as
with being the usual choice.
From the relation, this may also be expressed as
where is the algebraic residual at iteration.

Convergence in the symmetric positive definite case

If the system matrix is symmetric positive-definite, one can show convergence.
Let be the iteration matrix.
Then, convergence is guaranteed for
where is the maximal eigenvalue.
The spectral radius can be minimized for a particular choice of as follows
where is the matrix condition number.