Answer on Question #74014 – Math – Quantitative Methods
Question
For the linear system of equations [1 2 - 2, 1 1 1, 2 2 1][x y z] = [1 3 5] set up the gauss - jacobi and gauss - seidel iteration schemes in matrix form. also check the convergence of the two schemes.
Solution
Consider a system of linear equations A u = b Au = b A u = b with
A = [ 1 2 − 2 1 1 1 2 2 1 ] , u = [ x y z ] , b = [ 1 3 5 ] A = \left[ \begin{array}{ccc} 1 & 2 & -2 \\ 1 & 1 & 1 \\ 2 & 2 & 1 \end{array} \right], u = \left[ \begin{array}{c} x \\ y \\ z \end{array} \right], b = \left[ \begin{array}{c} 1 \\ 3 \\ 5 \end{array} \right] A = ⎣ ⎡ 1 1 2 2 1 2 − 2 1 1 ⎦ ⎤ , u = ⎣ ⎡ x y z ⎦ ⎤ , b = ⎣ ⎡ 1 3 5 ⎦ ⎤
1. The matrix form of Jacobi iterative method is
u k + 1 = D − 1 ( b − R u k ) u^{k+1} = D^{-1}(b - R u^k) u k + 1 = D − 1 ( b − R u k )
where u ( k ) u(k) u ( k ) is the k k k th approximation or iteration of u u u and u ( k + 1 ) u(k+1) u ( k + 1 ) is the next or k + 1 k+1 k + 1 iteration of u u u and A = D + R A = D + R A = D + R .
A = [ 1 2 − 2 1 1 1 2 2 1 ] = [ 1 0 0 0 1 0 0 0 1 ] + [ 0 2 − 2 1 0 1 2 2 0 ] A = \left[ \begin{array}{ccc} 1 & 2 & -2 \\ 1 & 1 & 1 \\ 2 & 2 & 1 \end{array} \right] = \left[ \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] + \left[ \begin{array}{ccc} 0 & 2 & -2 \\ 1 & 0 & 1 \\ 2 & 2 & 0 \end{array} \right] A = ⎣ ⎡ 1 1 2 2 1 2 − 2 1 1 ⎦ ⎤ = ⎣ ⎡ 1 0 0 0 1 0 0 0 1 ⎦ ⎤ + ⎣ ⎡ 0 1 2 2 0 2 − 2 1 0 ⎦ ⎤ D = [ 1 0 0 0 1 0 0 0 1 ] = D − 1 , R = [ 0 2 − 2 1 0 1 2 2 0 ] D = \left[ \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] = D^{-1}, \qquad R = \left[ \begin{array}{ccc} 0 & 2 & -2 \\ 1 & 0 & 1 \\ 2 & 2 & 0 \end{array} \right] D = ⎣ ⎡ 1 0 0 0 1 0 0 0 1 ⎦ ⎤ = D − 1 , R = ⎣ ⎡ 0 1 2 2 0 2 − 2 1 0 ⎦ ⎤
The convergence condition is: ρ ( B ) < 1 \rho(B) < 1 ρ ( B ) < 1 where B = D − 1 R B = D^{-1}R B = D − 1 R and ρ ( B ) = max { ∣ l 1 ∣ , ∣ l 2 ∣ , ∣ l 3 ∣ } \rho(B) = \max\{|l_1|, |l_2|, |l_3|\} ρ ( B ) = max { ∣ l 1 ∣ , ∣ l 2 ∣ , ∣ l 3 ∣ } , ∣ 1 , ∣ 2 , ∣ 3 } |1, |2, |3\} ∣1 , ∣2 , ∣3 } - eigenvalues of a matrix B B B .
l 1 , 2 , 3 = [ 0.000005 + 0.000009 i 0.000005 − 0.000009 i − 0.000011 ] → max { ∣ l 1 ∣ , ∣ l 2 ∣ , ∣ l 3 ∣ } = 0.000011 < 1 l_{1,2,3} = \left[ \begin{array}{c} 0.000005 + 0.000009i \\ 0.000005 - 0.000009i \\ -0.000011 \end{array} \right] \to \max\{|l_1|, |l_2|, |l_3|\} = 0.000011 < 1 l 1 , 2 , 3 = ⎣ ⎡ 0.000005 + 0.000009 i 0.000005 − 0.000009 i − 0.000011 ⎦ ⎤ → max { ∣ l 1 ∣ , ∣ l 2 ∣ , ∣ l 3 ∣ } = 0.000011 < 1 u k + 1 = D − 1 ( b − R u k ) = [ 1 0 0 0 1 0 0 0 1 ] ( [ 1 3 5 ] − [ 0 2 − 2 1 0 1 2 2 0 ] ⋅ u k ) = [ 1 3 5 ] − [ 0 2 − 2 1 0 1 2 2 0 ] ⋅ [ x k y k z k ] = [ 1 − 2 y k + 2 z k 3 − x k − z k 5 − 2 x k − 2 y k ] \begin{array}{l}
u^{k+1} = D^{-1}(b - R u^k) = \left[ \begin{array}{ccc} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] \left( \left[ \begin{array}{c} 1 \\ 3 \\ 5 \end{array} \right] - \left[ \begin{array}{ccc} 0 & 2 & -2 \\ 1 & 0 & 1 \\ 2 & 2 & 0 \end{array} \right] \cdot u^k \right) \\
= \left[ \begin{array}{c} 1 \\ 3 \\ 5 \end{array} \right] - \left[ \begin{array}{ccc} 0 & 2 & -2 \\ 1 & 0 & 1 \\ 2 & 2 & 0 \end{array} \right] \cdot \left[ \begin{array}{c} x^k \\ y^k \\ z^k \end{array} \right] = \left[ \begin{array}{c} 1 - 2y^k + 2z^k \\ 3 - x^k - z^k \\ 5 - 2x^k - 2y^k \end{array} \right]
\end{array} u k + 1 = D − 1 ( b − R u k ) = ⎣ ⎡ 1 0 0 0 1 0 0 0 1 ⎦ ⎤ ⎝ ⎛ ⎣ ⎡ 1 3 5 ⎦ ⎤ − ⎣ ⎡ 0 1 2 2 0 2 − 2 1 0 ⎦ ⎤ ⋅ u k ⎠ ⎞ = ⎣ ⎡ 1 3 5 ⎦ ⎤ − ⎣ ⎡ 0 1 2 2 0 2 − 2 1 0 ⎦ ⎤ ⋅ ⎣ ⎡ x k y k z k ⎦ ⎤ = ⎣ ⎡ 1 − 2 y k + 2 z k 3 − x k − z k 5 − 2 x k − 2 y k ⎦ ⎤
2. The matrix form of gauss - seidel iterative method is
u k + 1 = L − 1 ( b − M u k ) u^{k+1} = L^{-1}(b - M u^k) u k + 1 = L − 1 ( b − M u k )
where u ( k ) u(k) u ( k ) is the kth approximation or iteration of u u u and u ( k + 1 ) u(k+1) u ( k + 1 ) is the next or k + 1 k+1 k + 1 iteration of u u u and A = L + M A = L + M A = L + M .
A = [ 1 2 − 2 1 1 1 2 2 1 ] = [ 1 0 0 1 1 0 2 2 1 ] + [ 0 2 − 2 0 0 1 0 0 0 ] A = \left[ \begin{array}{ccc} 1 & 2 & -2 \\ 1 & 1 & 1 \\ 2 & 2 & 1 \end{array} \right] = \left[ \begin{array}{ccc} 1 & 0 & 0 \\ 1 & 1 & 0 \\ 2 & 2 & 1 \end{array} \right] + \left[ \begin{array}{ccc} 0 & 2 & -2 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{array} \right] A = ⎣ ⎡ 1 1 2 2 1 2 − 2 1 1 ⎦ ⎤ = ⎣ ⎡ 1 1 2 0 1 2 0 0 1 ⎦ ⎤ + ⎣ ⎡ 0 0 0 2 0 0 − 2 1 0 ⎦ ⎤ L = [ 1 0 0 1 1 0 2 2 1 ] , L − 1 = [ 1 0 0 − 1 1 0 0 − 2 1 ] , M = [ 0 2 − 2 0 0 1 0 0 0 ] L = \left[ \begin{array}{ccc} 1 & 0 & 0 \\ 1 & 1 & 0 \\ 2 & 2 & 1 \end{array} \right], L^{-1} = \left[ \begin{array}{ccc} 1 & 0 & 0 \\ -1 & 1 & 0 \\ 0 & -2 & 1 \end{array} \right], M = \left[ \begin{array}{ccc} 0 & 2 & -2 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{array} \right] L = ⎣ ⎡ 1 1 2 0 1 2 0 0 1 ⎦ ⎤ , L − 1 = ⎣ ⎡ 1 − 1 0 0 1 − 2 0 0 1 ⎦ ⎤ , M = ⎣ ⎡ 0 0 0 2 0 0 − 2 1 0 ⎦ ⎤
The convergence condition is: ρ ( B ) < 1 \rho(B) < 1 ρ ( B ) < 1 where B = L − 1 M B = L^{-1}M B = L − 1 M and ρ ( B ) = max { ∣ l 1 ∣ , ∣ l 2 ∣ , ∣ l 3 ∣ } \rho(B) = \max\{|l_1|, |l_2|, |l_3|\} ρ ( B ) = max { ∣ l 1 ∣ , ∣ l 2 ∣ , ∣ l 3 ∣ } , I1, I2, I3 - eigenvalues of a matrix B.
l 1 , 2 , 3 = [ 0 − 2 − 2 ] → max { ∣ l 1 ∣ , ∣ l 2 ∣ , ∣ l 3 ∣ } = 2 > 1 l_{1,2,3} = \left[ \begin{array}{c} 0 \\ -2 \\ -2 \end{array} \right] \to \max \bigl\{|l_1|, |l_2|, |l_3|\bigr\} = 2 > 1 l 1 , 2 , 3 = ⎣ ⎡ 0 − 2 − 2 ⎦ ⎤ → max { ∣ l 1 ∣ , ∣ l 2 ∣ , ∣ l 3 ∣ } = 2 > 1 u k + 1 = L − 1 ( b − M u k ) = [ 1 0 0 − 1 1 0 0 − 2 1 ] ( [ 1 3 5 ] − [ 0 2 − 2 0 0 1 0 0 0 ] ⋅ u k ) u^{k+1} = L^{-1}(b - M u^k) = \left[ \begin{array}{ccc} 1 & 0 & 0 \\ -1 & 1 & 0 \\ 0 & -2 & 1 \end{array} \right] \left( \left[ \begin{array}{c} 1 \\ 3 \\ 5 \end{array} \right] - \left[ \begin{array}{ccc} 0 & 2 & -2 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{array} \right] \cdot u^k \right) u k + 1 = L − 1 ( b − M u k ) = ⎣ ⎡ 1 − 1 0 0 1 − 2 0 0 1 ⎦ ⎤ ⎝ ⎛ ⎣ ⎡ 1 3 5 ⎦ ⎤ − ⎣ ⎡ 0 0 0 2 0 0 − 2 1 0 ⎦ ⎤ ⋅ u k ⎠ ⎞ = [ 1 0 0 − 1 1 0 0 − 2 1 ] [ 1 − 2 y k + 2 z k 3 − z k 5 ] = [ 1 − 2 y k + 2 z k 2 + 2 y k − 3 z k 2 z k − 1 ] = \left[ \begin{array}{ccc} 1 & 0 & 0 \\ -1 & 1 & 0 \\ 0 & -2 & 1 \end{array} \right] \left[ \begin{array}{c} 1 - 2y^k + 2z^k \\ 3 - z^k \\ 5 \end{array} \right] = \left[ \begin{array}{c} 1 - 2y^k + 2z^k \\ 2 + 2y^k - 3z^k \\ 2z^k - 1 \end{array} \right] = ⎣ ⎡ 1 − 1 0 0 1 − 2 0 0 1 ⎦ ⎤ ⎣ ⎡ 1 − 2 y k + 2 z k 3 − z k 5 ⎦ ⎤ = ⎣ ⎡ 1 − 2 y k + 2 z k 2 + 2 y k − 3 z k 2 z k − 1 ⎦ ⎤
**Answer:**
1. jacobi scheme: [ x k + 1 y k + 1 z k + 1 ] = [ 1 − 2 y k + 2 z k 3 − x k − z k 5 − 2 x k − 2 y k ] \begin{bmatrix} x^{k+1} \\ y^{k+1} \\ z^{k+1} \end{bmatrix} = \begin{bmatrix} 1 - 2y^k + 2z^k \\ 3 - x^k - z^k \\ 5 - 2x^k - 2y^k \end{bmatrix} ⎣ ⎡ x k + 1 y k + 1 z k + 1 ⎦ ⎤ = ⎣ ⎡ 1 − 2 y k + 2 z k 3 − x k − z k 5 − 2 x k − 2 y k ⎦ ⎤ , the convergence condition is satisfied
2. gauss - seidel scheme: [ x k + 1 y k + 1 z k + 1 ] = [ 1 − 2 y k + 2 z k 2 + 2 y k − 3 z k 2 z k − 1 ] \begin{bmatrix} x^{k+1} \\ y^{k+1} \\ z^{k+1} \end{bmatrix} = \begin{bmatrix} 1 - 2y^k + 2z^k \\ 2 + 2y^k - 3z^k \\ 2z^k - 1 \end{bmatrix} ⎣ ⎡ x k + 1 y k + 1 z k + 1 ⎦ ⎤ = ⎣ ⎡ 1 − 2 y k + 2 z k 2 + 2 y k − 3 z k 2 z k − 1 ⎦ ⎤ , the convergence condition is not satisfied
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