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Perhaps the simplest iterative method for solving *A*** x** =

Given a current approximation

**x**^{(k)} = (*x*_{1}^{(k)}, *x*_{2}^{(k)}, *x*_{3}^{(k)}, …, *x*_{n}^{(k)})

for ** x**, the strategy of Jacobi's Method is to use the first equation and the current values of

*x*^{(k+1)} = (*x*_{1}^{(k+1)}, *x*_{2}^{(k+1)}, …, *x*_{n}^{(k+1)})

in

This system can also be written as

To be clear, the subscript *i* means that *x*_{i}^{(k)} is the *i* ^{th} element of vector

*x*^{(k)} = (*x*_{1}^{(k)}, *x*_{2}^{(k)}, …, *x*_{i}^{(k)}, …, *x*_{n}^{(k)} ),

and superscript *k* corresponds to the particular iteration (not the *k*^{th} power of *x*_{i} ).

If we write *D*, *L,* and *U* for the diagonal, strict lower triangular and strict upper triangular and parts of *A*, respectively,

then Jacobi’s Method can be written in matrix-vector notation as

Let's apply Jacobi's Method to the system

At each step, given the current values *x*_{1}^{(k)}, *x*_{2}^{(k)}, *x*_{3}^{(k)}, we solve for *x*_{1}^{(k+1)}, *x*_{2}^{(k+1)}, and *x*_{3}^{(k+1)} in

So, if our initial guess *x*^{(0)} = (*x*_{1}^{(0)}, *x*_{2}^{(0)}, *x*_{3}^{(0)}) is the zero vector **0** = (0,0,0) — a common initial guess unless we have some additional information that leads us to choose some other — then we find *x*^{(1)} = (*x*_{1}^{(1)}, *x*_{2}^{(1)}, *x*_{3}^{(1)} ) by solving

So *x*^{(1)} = (*x*_{1}^{(1)}, *x*_{2}^{(1)}, *x*_{3}^{(1)}) = (3/4, 9/6, −6/7) ≈ (0.750, 1.500, −0.857). We iterate this process to find a sequence of increasingly better approximations *x*^{(0)}, *x*^{(1)}, *x*^{(2)}, … . We show the results in the table below, with all values rounded to 3 decimal places.

We are interested in the error ** e** at each iteration between the true solution

The norm of a vector ||* x*|| tells us how big the vector is as a whole (as opposed to how large each element of the vector is). The vector norm most commonly used in linear algebra is the

For example, if ** x** = (1, 2, −1), then

In this module, we will always use the *l*^{2} norm (including for matrix norms in subsequent tutorials), so that || || always signifies || ||_{2}.

For our purposes, we observe that ||* x*|| will be small exactly when each of the elements

k |
x^{(k)} |
x^{(k)} − x^{(k-1)} |
e^{(k)} = − xx^{(k)} |
||e^{(k)}|| |
||||||
---|---|---|---|---|---|---|---|---|---|---|

0 | -0.000 | -0.000 | -0.000 | − | − | − | -0.000 | -0.000 | -1.000 | 2.449 |

1 | 0.750 | 1.500 | -0.857 | -0.000 | -0.000 | -0.857 | 0.250 | 0.500 | -0.143 | 0.557 |

2 | 0.911 | 1.893 | -0.964 | 0.161 | 0.393 | -0.107 | 0.089 | 0.107 | -0.036 | 0.144 |

3 | 0.982 | 1.964 | -0.997 | 0.071 | 0.071 | -0.033 | 0.018 | 0.036 | -0.003 | 0.040 |

4 | 0.992 | 1.994 | -0.997 | 0.010 | 0.029 | 0.000 | 0.008 | 0.006 | -0.003 | 0.011 |

5 | 0.999 | 1.997 | -1.000 | 0.007 | 0.003 | -0.003 | 0.001 | 0.003 | 0.000 | 0.003 |

6 | 0.999 | 2.000 | -1.000 | 0.000 | 0.003 | 0.001 | 0.000 | 0.001 | 0.000 | 0.001 |

7 | 1.000 | 2.000 | -1.000 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

8 | 1.000 | 2.000 | -1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

For this example, we stop iterating after all three ways of measuring the current error,

**x**^{(k)} − **x**^{(k-1)}, *e*^{(k)}, and ||*e*^{(k)}||,

equal 0 to three decimal places. In practice, you would normally choose a single measurement of error to determine when to stop.

David M. Strong, "Iterative Methods for Solving [i]Ax[/i] = [i]b[/i] - Jacobi's Method," *Convergence* (July 2005)

Journal of Online Mathematics and its Applications