Jacobi method
In numerical linear algebra, the Jacobi method is an iterative algorithm for determining the solutions of a 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:
Then A can be decomposed into a diagonal component D, and the remainder R:
The solution is then obtained iteratively via
where is the kth approximation or iteration of and is the next or k + 1 iteration of . The element-based formula is thus:
The computation of xi(k+1) requires each element in x(k) except itself. Unlike the Gauss–Seidel method, we can't overwrite xi(k) with xi(k+1), 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: initial guess to the solution, (diagonal dominant) matrix , right-hand side vector , convergence criterion Output: solution when convergence is reached 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 end
Convergence
The standard convergence condition (for any iterative method) is when the spectral radius of the iteration matrix is less than 1:
A sufficient (but not necessary) 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,
Example
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 . Note that where and are the strictly lower and upper parts of . 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 (i.e., until is small). The solution after 25 iterations is
Another example
Suppose we are given the following linear system:
If we choose (0, 0, 0, 0) 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.
0.6 | 2.27272 | -1.1 | 1.875 |
1.04727 | 1.7159 | -0.80522 | 0.88522 |
0.93263 | 2.05330 | -1.0493 | 1.13088 |
1.01519 | 1.95369 | -0.9681 | 0.97384 |
0.98899 | 2.0114 | -1.0102 | 1.02135 |
The exact solution of the system is (1, 2, −1, 1).
An example using Python and Numpy
The following numerical procedure simply iterates to produce the solution vector.
import numpy as np
ITERATION_LIMIT = 1000
# initialize the matrix
A = np.array([[10., -1., 2., 0.],
[-1., 11., -1., 3.],
[2., -1., 10., -1.],
[0.0, 3., -1., 8.]])
# initialize the RHS vector
b = np.array([6., 25., -11., 15.])
# prints the system
print("System:")
for i in range(A.shape[0]):
row = ["{}*x{}".format(A[i, j], j + 1) for j in range(A.shape[1])]
print(" + ".join(row), "=", b[i])
print()
x = np.zeros_like(b)
for it_count in range(ITERATION_LIMIT):
print("Current solution:", x)
x_new = np.zeros_like(x)
for i in range(A.shape[0]):
s1 = np.dot(A[i, :i], x[:i])
s2 = np.dot(A[i, i + 1:], x[i + 1:])
x_new[i] = (b[i] - s1 - s2) / A[i, i]
if np.allclose(x, x_new, atol=1e-10, rtol=0.):
break
x = x_new
print("Solution:")
print(x)
error = np.dot(A, x) - b
print("Error:")
print(error)
Produces the output:
System: 10.0*x1 + -1.0*x2 + 2.0*x3 + 0.0*x4 = 6.0 -1.0*x1 + 11.0*x2 + -1.0*x3 + 3.0*x4 = 25.0 2.0*x1 + -1.0*x2 + 10.0*x3 + -1.0*x4 = -11.0 0.0*x1 + 3.0*x2 + -1.0*x3 + 8.0*x4 = 15.0 Current solution: [ 0. 0. 0. 0.] Current solution: [ 0.6 2.27272727 -1.1 1.875 ] Current solution: [ 1.04727273 1.71590909 -0.80522727 0.88522727] Current solution: [ 0.93263636 2.05330579 -1.04934091 1.13088068] Current solution: [ 1.01519876 1.95369576 -0.96810863 0.97384272] Current solution: [ 0.9889913 2.01141473 -1.0102859 1.02135051] Current solution: [ 1.00319865 1.99224126 -0.99452174 0.99443374] Current solution: [ 0.99812847 2.00230688 -1.00197223 1.00359431] Current solution: [ 1.00062513 1.9986703 -0.99903558 0.99888839] Current solution: [ 0.99967415 2.00044767 -1.00036916 1.00061919] Current solution: [ 1.0001186 1.99976795 -0.99982814 0.99978598] Current solution: [ 0.99994242 2.00008477 -1.00006833 1.0001085 ] Current solution: [ 1.00002214 1.99995896 -0.99996916 0.99995967] Current solution: [ 0.99998973 2.00001582 -1.00001257 1.00001924] Current solution: [ 1.00000409 1.99999268 -0.99999444 0.9999925 ] Current solution: [ 0.99999816 2.00000292 -1.0000023 1.00000344] Current solution: [ 1.00000075 1.99999868 -0.99999899 0.99999862] Current solution: [ 0.99999967 2.00000054 -1.00000042 1.00000062] Current solution: [ 1.00000014 1.99999976 -0.99999982 0.99999975] Current solution: [ 0.99999994 2.0000001 -1.00000008 1.00000011] Current solution: [ 1.00000003 1.99999996 -0.99999997 0.99999995] Current solution: [ 0.99999999 2.00000002 -1.00000001 1.00000002] Current solution: [ 1. 1.99999999 -0.99999999 0.99999999] Current solution: [ 1. 2. -1. 1.] Solution: [ 1. 2. -1. 1.] Error: [ -2.81440107e-08 5.15706873e-08 -3.63466359e-08 4.17092547e-08]
Weighted Jacobi method
The weighted Jacobi iteration uses a parameter to compute the iteration as
with being the usual choice.[1]
Convergence in the symmetric positive definite case
In case that the system matrix is of symmetric positive-definite type 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.
See also
- Gauss–Seidel method
- Successive over-relaxation
- Iterative method § Linear systems
- Gaussian Belief Propagation
- Matrix splitting
References
- ^ Saad, Yousef (2003). Iterative Methods for Sparse Linear Systems (2 ed.). SIAM. p. 414. ISBN 0898715342.
External links
- "Jacobi method", Encyclopedia of Mathematics, EMS Press, 2001 [1994]
- This article incorporates text from the article Jacobi_method on CFD-Wiki that is under the GFDL license.
- Black, Noel; Moore, Shirley; and Weisstein, Eric W. "Jacobi method". MathWorld.
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- Numerical matrix inversion