Keywords: ELMRES, preconditioned technique, Hessenberg algorithm, least square problems
Applied Mathematics and Physics, 2013 1 (4),
pp 9093.
DOI: 10.12691/amp141
Received September 19, 2013; Revised October 10, 2013; Accepted October 11, 2013
Copyright © 2014 Science and Education Publishing. All Rights Reserved.
1. Introduction
Many applications require the solution of several sparse systems of linear equations
 (1) 
with the coefficient matrix , the right hand side vector and the unknown solution vector .
There are several different iterative algorithms for computing the solution of (1). Among them, krylov subspace methods, like algorithms from ^{[1, 3, 5, 6]}, are more popular solvers for this sort of equations. The krylov subspace is defined as
 
which is a practical subspace for modification of new approximations with where is the initial guess. ELMRES was proposed by G. Howell and D. Stephens ^{[3]} in 2000. The implementation of ELMRES is approximately similar to GMRES so that in both methods the original problem (1) is transferred to an upper Hessenberg least square problem with dimension which whilst GMRES uses the Arnoldi orthogonalization algorithm for reduction and ELMRES uses the Hessenberg decomposition algorithm. The construction of this paper is as follows: In section 2, the ELMRES is discussed. In section 3, the left and right preconditioner methods are described shortly and some popular applicable matrices for these methods which are also practical for ELMRES are shown. In the next section, the proposed preconditioned matrices are applied and then they are tested by some numerical examples.
2. Elmres
Here the ELMRES algorithm is described briefly. In ELMRES method a basis constructs for the krylov subspace by the Hessenberg algorithm. Suppose . Now, the procedure for extending the dimension of the subspace is shown in the following algorithm ^{[3]}.
2.1. Algorithm 1: Hessenberg Decomposition Algorithm1. vector and set and .
2. for
1.
2.
End
3.
In the algorithm 1, it is shown that the step of the for loop zeros the entry of where . At the end of algorithm, the entry of becomes . In this case, . If the partial pivoting is employed, the relation
 (2) 
is concluded,(see ^{[3]}).
An elementary similarity transformation is of the form
 (3) 
where is a column vector with zeros as its first entries and is a vector with its only nonzero entry in the position.
Without pivoting condition, reduction by elementary similarity transformations to upper hessenberg form is accomplished by
 (4) 
which form the structures of s and upper hessenberg matrix we have
By definition of and the following relation is obtained
 (5) 
By using this important relation, we reach to the reduced problem
The approximate solution is where .
Wilkinson ^{[8]} suggested Hessenberg reduction with implicit pivoting avoids the breakdown in decomposition process. Here, the ELMRES with implicit pivoting ^{[3]} is shown.
2.2. Algorithm 2: ELMRES with Implicit Pivoting1. Choose , set
set so that .
2. For , until satisfied do
,
For ,
,
End
,
For do
,
,
End
set so that .
,
.
End
Choose to minimize .
3. From the approximate solution .
For ,
,
End
For more information about ELMRES and its properties see ^{[3]}.
3. Preconditioned ELMRES
The structure of GMRES is approximately similar to ELMRES. So, some prediction GMRES techniques may be useful for ELMRES. Here, GMRES with right preconditioners are shown and some suitable matrices for preconditioned ELMRES are suggested.
Let is a preconditioned matrix. In left preconditioned GMRES, the original equation is replaced with and the orthogonalization procedure is run so that a normalized basis vectors is obtained for the preconditioned krylov subspace
by the Arnoldi algorithm. So, we have to apply GMRES algorithm just for the new linear system of equations.
Furthermore, the right preconditioned GMRES algorithm is based on solving
 (6) 
As the right preconditioned GMRES is more practical, its algorithm is written below ^{[6]}.
2.3. Algorithm 3: Right Preconditioned GMRES 1. Choose , set.
2. For
,
For
do , ..,
End
,
If set and goto 3,
End
3. and .
From the above algorithm, the vector is not computed explicitly and the Arnoldi process generates a basis vector set for the preconditioned subspace
.
In this case, the residual norm is now relative to the initial system (1).
GaussSeidel and SOR are two popular classical iterative methods for solving (1) while using the matrices regard to the structure of these methods as some preconditioners are popular techniques for GMRES. Now, we apply the preconditioned matrices and obtained from GaussSeidel and SOR respectively. Suppose where , and are the elements lower than the diagonal, the diagonal elements and the elements upper than the diagonal of matrix respectively. So, and are defined as follows ^{[6]},
 (7) 
4. Numerical Tests
The In this section, some wellknown numerical examples are applied to test the effect of preconditioned techniques on the convergence speed of ELMRES. For simplicity, is chosen as the initial guess in these experiments. These algorithms are implemented in Matlab 7.3 on a Pentium 4 PC with 1 GB of RAM. The stopping criterion is used for all implementations.
Example 1: For construction of linear system of equations (1), Let be an even integer and denote by and , respectively, the identity and zero matrices. Define also the matrices and as in
Finally, construct the nonsymmetric matrix as in
The matrix is obtained by first discretizing the Poisson equation
for
with Neumann boundary conditions
on a uniform grid of mesh size via central differences, and then by taking the unknowns in the redblack ordering. This problem was also considered by Saberi Najafi and Zareamoghaddam in ^{[7]} for testing another GMRES implementation. Note that is singular with a 1D null space spanned by the vector .
In our numerical experiments, for the first exam we took which the corresponding square matrix is in order . Table1shows the numerical results of different ELMRES implementations applied for the linear problem with order .
Table 1. Numerical results of example1
Table 2. Numerical results of example2
Example 2: There are many different numerical examples for system of linear equations which among them, "Regularization Tools" of Prof. Hansen ^{[2]} is one popular package for illposed linear system of equations. In this example, the main source code of one discrete image deblurring problem is selected from this package ^{[2]}. The Matlab code "blure" with detailed parameters , and , i.e. is used to generate the square matrix with the right hand side vector . This problem has been contaminated by blur and noise. Numerical results of this test have been illustrated in Table 2.
5. Conclusion
Elementary residual method is rather a new krylov subspace method for solving linear system of equations. In this paper, some right preconditioned matrices have been tested for ELMRES to solve different linear system of equations and the results show that the convergence of ELMRES has also been influenced by some appropriate preconditioning matrices.
Acknowledgement
Hereby, the authors want to thank of Islamic Azad University, Bardaskan Branch because of the financial support.
References
[1]  Freund R., Nachtigal N., QMR: A QuasiMinimal Residual Method for NonHermitian Linear Systems, Numer. Math. 60(1991) 315339. 
 In article  

[2]  Hansen P. C., Regularization Tools Version 4.0 for Matlab 7.3, Numerical Algorithms, 46 (2007) 189194. 
 In article  

[3]  Howell G., Stephens D., ELMRES: an oblique projection solver for sparse systems of linear equations, technical report, Florida Institute of Technology, 2000. 
 In article  

[4]  Lewis B., Reichel L., ArnoldiTikhonov regularization methods, J. Comput. Appl. Math., 226 (2009) 92102. 
 In article  

[5]  Paige C. C., Saunders M. A., LSQR: an algorithm for sparse linear equations and sparse least squares, ACM Trans. Math. Software 8 (1982) 4371. 
 In article  

[6]  Saad Y., Iterative method for sparse linear systems. PWS publishing company, a division of international Thomson publishing Inc, U.S.A, 1996. 
 In article  

[7]  Saberi Najafi H., Zareamoghaddam H., A new computational GMRES method, Appl. Math. Comput. 2 (2008) 527534. 
 In article  CrossRef 

[8]  Wilkinson J. H., the Algebraic Eigenvalue Problem. Clarendon Press, Oxford, England, 1965. 
 In article  
