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Jacobi method

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Revision as of 20:33, 15 December 2005 by Tsaad (Talk | contribs)
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We seek the solution to set of linear equations:

 A \cdot X = Q

For the given matrix A and vectors X and Q.
In matrix terms, the definition of the Jacobi method can be expressed as :
x^{(k)}  = D^{ - 1} \left( {L + U} \right)x^{(k - 1)}  + D^{ - 1} Q
Where D,L and U represent the diagonal, lower triangular and upper triangular matrices of coefficient matrix A and k is iteration counter.


Chose an intital guess X^{0} to the solution
for k := 1 step 1 untill convergence do
for i := 1 step until n do
 \sigma = 0
for j := 1 step until n do
if j != i then
 \sigma  = \sigma  + a_{ij} x_j^{(k-1)}
end if
end (j-loop)
  x_i^{(k)}  = {{\left( {q_i  - \sigma } \right)} \over {a_{ii} }}
end (i-loop)
check if convergence is reached
end (k-loop)

Note: The major difference between the Gauss-Seidel method and Jacobi method lies in the fact that for Jacobi method the values of solution of previous iteration (here k) are used, where as in Gauss-Seidel method the latest available values of solution vector X are used.

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