Chapter 10
Jacobi and Gauss-Seidel Methods
10.1 The Jacobi and Gauss-Seidel Methods: An Example ............ 148
10.2 Splitting Methods ................................................ 148
10.3 Some Examples of Splitting Methods ............................ 150
10.4 Jacobi’s Algorithm and JOR ..................................... 151
10.4.1 The JOR in the Nonnegative-Definite Case ............. 152
10.5 The Gauss-Seidel Algorithm and SOR .......................... 153
10.5.1 The Nonnegative-Definite Case ......................... 153
10.5.2 The GS Algorithm as ART .............................. 155
10.5.3 Successive Overrelaxation ............................... 155
10.5.4 The SOR for Nonnegative-Definite Q ................... 156
10.6 Summary ......................................................... 156
In this chapter we examine two well known iterative algorithms for solving
square systems of linear equations, the Jacobi method and the Gauss-Seidel
method, in terms of averaged and paracontractive operators. Both these
algorithms are easy to describe and to motivate. They both require not
only that the system be square, that is, have the same number of unknowns
as equations, but satisfy additional constraints needed for convergence.
Linear systems Ax = b need not be square but can be associated with
two square systems, A
Ax = A
b, the so-called normal equations,and
AA
z = b, sometimes called the Bj¨orck-Elfving equations [107]. Both the
Jacobi and the Gauss-Seidel algorithms can be modified to apply to any
square system of linear equations, Sz = h. The resulting algorithms, the
Jacobi overrelaxation (JOR) and successive overrelaxation (SOR) methods,
involve the choice of a parameter. The JOR and SOR will converge for more
general classes of matrices, provided that the parameter is appropriately
chosen. Particular cases of the Jacobi and the Gauss-Seidel methods are
equivalent to the Landweber algorithm and the ART, respectively.
When we say that an iterative method is convergent, or converges, under
certain conditions, we mean that it converges for any consistent system of
the appropriate type, and for any starting vector; any iterative method
will converge if we begin at the right answer. We assume throughout this
chapter that A is an I by J matrix.
147
148 Iterative Optimization in Inverse Problems
10.1 The Jacobi and Gauss-Seidel Methods: An
Example
Suppose we wish to solve the 3 by 3 system
S
11
z
1
+ S
12
z
2
+ S
13
z
3
= h
1
S
21
z
1
+ S
22
z
2
+ S
23
z
3
= h
2
S
31
z
1
+ S
32
z
2
+ S
33
z
3
= h
3
, (10.1)
which we can rewrite as
z
1
= S
1
11
[h
1
S
12
z
2
S
13
z
3
]
z
2
= S
1
22
[h
2
S
21
z
1
S
23
z
3
]
z
3
= S
1
33
[h
3
S
31
z
1
S
32
z
2
], (10.2)
assuming that the diagonal terms S
mm
are not zero. Let z
0
=(z
0
1
,z
0
2
,z
0
3
)
T
be an initial guess for the solution. We then insert the entries of z
0
on the
right sides and use the left sides to define the entries of the next guess z
1
.
This is one full cycle of Jacobi’s method.
The Gauss-Seidel method is similar. Let z
0
=(z
0
1
,z
0
2
,z
0
3
)
T
be an initial
guess for the solution. We then insert z
0
2
and z
0
3
on the right side of the
first equation, obtaining a new value z
1
1
on the left side. We then insert
z
0
3
and z
1
1
on the right side of the second equation, obtaining a new value
z
1
2
on the left. Finally, we insert z
1
1
and z
1
2
into the right side of the third
equation, obtaining a new z
1
3
on the left side. This is one full cycle of the
Gauss-Seidel (GS) method.
10.2 Splitting Methods
The Jacobi and the Gauss-Seidel methods are particular cases of a more
general approach known as splitting methods. Splitting methods apply to
square systems of linear equations. Let S be an arbitrary N by N square
matrix, written as S = M K. Then the linear system of equations Sz = h
is equivalent to Mz = Kz+h.IfM is invertible, then we can also write z =
M
1
Kz + M
1
h. This last equation suggests a class of iterative methods

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