Table of Contents Author Guidelines Submit a Manuscript
Journal of Mathematics
Volume 2018, Article ID 8175935, 6 pages
https://doi.org/10.1155/2018/8175935
Research Article

High-Order Iterative Methods for the DMP Inverse

1School of Science, Guangxi University for Nationalities, Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning 530006, China
2School of Science, Guangxi University for Nationalities, Guangxi, China

Correspondence should be addressed to Xiaoji Liu; moc.621@27uilijoaix

Received 1 December 2017; Revised 4 March 2018; Accepted 26 March 2018; Published 7 May 2018

Academic Editor: Shwetabh Srivastava

Copyright © 2018 Xiaoji Liu and Naping Cai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We investigate two iterative methods for computing the DMP inverse. The necessary and sufficient conditions for convergence of our schemes are considered and the error estimate is also derived. Numerical examples are given to test the accuracy and effectiveness of our methods.

1. Introduction

Let be the set of all complex matrices with rank . For any given a matrix , let , , and be the range space, the null space, and the Frobenius norm of matrix , respectively. For a nonnegative integer , if , then is called the index of and . In recent years, the generalized inverse has been applied in many fields of engineering and technology, such as control[1], the least squares problem [2, 3], matrix decomposition [4], image restoration, statistics (see [5]), and preconditioning [68]. In particular, -inverse plays an important role in stable approximations of ill-posed problems (see [1, 9]) and in linear and nonlinear problems [6, 10]. In [11], Baksalary and Trenkle investigate the core inverse.

For a given matrix , there exists matrix satisfying (see [8])(1),(2),(3),(4),

where is called the Moore-Penrose inverse of , denoted by , and it is unique. For a given matrix , there exists a matrix satisfying(1),(2),(3),

where is called the Drain inverse of , denoted by , and it is unique. Based on the Drazin inverse and the Moore-Penrose (MP) inverse, a new generalized inverse is defined in [12] as (see also [13, 14]): for a matrix , there is matrix satisfying (see [12])(1),(2),(3),

where is called DMP inverse, denoted by , and it is unique. It is shown that in [12]. In [15], Yu and Deng get some characterizations of DMP inverse in a Hilbert space. By using idempotent element, some new properties of DMP inverse are given in [16].

So far, there are few results on computation of the DMP inverse by the iterative methods given in [1722]. Recently, a family of higher-order convergent iterative methods are developed in [23] and applied to compute the Moore-Penrose inverse; the method is extended to compute the generalized inverse in [20]. In this paper, we develop two iterative methods to compute the DMP inverse of a given matrix . The proposed method (I) is higher-order and the proposed method (II) can be implemented easily.

The paper is organized as follows. The proposed iterative methods for computing DMP inverse are given and some lemmas used for its convergence analysis are given in Section 2. The stability and convergence analysis of our scheme (1) and (4) are given, and numerical examples are given to test the corresponding theoretical results in Sections 3 and 4, respectively.

2. Preliminaries and Iterative Scheme

Lemma 1 (see [23]). If and , then(i),(ii).

Lemma 2 (see [12]). Let , . If exist, then(i),(ii).

As in [20], we develop a iterative scheme to compute the DMP inverse as follows.

Scheme I:where , , and . Following the line [21], we develop the iterative following scheme as Let and, by , we have Thus, an efficient high-order iterative method can be written as follows.

Scheme II:The iterative method given in (4) is applied to compute the Drazin inverse by [21]. Here, we use the sequence of iterative to compute the DMP inverse.

3. Scheme I for the DMP Inverse

In this section, we consider the numerical analysis of Scheme I (1) and present a numerical example to test our numerical theoretical results.

3.1. Stability and Convergence Analysis

Theorem 3. Let with . For an arbitrary positive integer , the sequence (1) converges if only if . Moreover, we have

Proof. Let for convenience. If , , then . From Lemma 2, we attain .
Now, we test by using mathematical induction. Assume that for any positive integer . By Lemma 2, we have Let ; thenSimilarly, by Lemma 2 and (7), we derive and Thus, we have Next, we investigate the necessary and sufficient condition for convergent property of Scheme I (1). Assume that the sequence converges to . Thus, , while . Therefore, we have .
Conversely, let for some scalar . Then . Thus, we have and

As in [24], we show that Scheme I (1) is asymptotically stable as the following result.

Theorem 4. Let and let the sequence be generated by (1) with an initial If , , and , then Scheme I (1) is asymptotically stable.

Proof. Let be the numerical perturbation of in Scheme I (1). Thus, it can be written into as . Here, we perform a first-order error analysis; that is, we formally neglect quadratic or higher terms. The manipulation is meaningful, while is sufficiently small. Further, we have Let ; we have Similarly, we have andBy (13) and , we derive By (7), we obtain Thus, we derive We can conclude that the perturbation at the iterate is bounded. Therefore, the sequence generated by (1) is asymptotically stable.

3.2. Numerical Example

Here is an example for computing DMP inverse in the iterative method (1).

Example 1. Let where and . Thus, . To test the high accuracy and efficiency of Scheme I (1), the DMP inverse of is given as Here, we apply Scheme I given in (1) to compute the DMP inverse with . The errors and are given in Table 1. From the numerical results note that (1) converges to and it has high-order accuracy.

Table 1: Numerical results of Scheme I with in Example 1.

4. Scheme II for the DMP Inverse

Here, the numerical analysis of Scheme II is derived and a numerical example is given to test our numerical theoretical results. Note that it is difficult to construct a projection given in Theorems 3 and 4 with satisfying .

4.1. Stability and Convergence Analysis

Theorem 2. Let with ; the sequence defined by Scheme II. If initial approximation satisfies and , then Scheme II converges to and

Proof. Similar to the proof of Theorem 9 in [21], let for ; thenThus, we have Since , we obtain that Similarly, . So we can get the conclusion , .
Since , , we attain . By Lemma 2, we have and So, we have
If the condition is satisfied, then when . In other words, , the scheme satisfies the following error estimate:

Theorem 3. Let be a singular square matrix with and the sequence defined by Scheme II. If the initial approximation is chosen such that and , then the order of convergence of the sequence is nine.

Proof. Let and ; we have .
Now, let ; we have Since DMP inverse is a special of -inverse, by citing the proof of Theorem 3.1 in [25], we have and and ).
Since when , we have .
It would be easy to find the error inequality of the high-order iterative as follows: Thus, the sequence converges to DMP inverse and the convergence order is nine.

It is easy to find with satisfying . So the method to compute DMP inverse is more convenient than another.

In what follows, we investigate the stability of the iterative method (4).

Theorem 4. Let ; the sequence defined by Scheme II with the initial approximation satisfies that is asymptotically stable for computing DMP inverse.

Proof. Let be the numerical perturbation introduced in Scheme II. Next, the modified valueappears instead of the exact value . Here, we formally neglect quadratic or higher terms such as . This formal manipulation is meaningful if is sufficiently small and further has , then, , when , .
Simplifying (28) we attain Using the matrix identity, we have , then We can conclude that the perturbation at the iterate is bounded. Therefore, the sequence generated by Scheme II is asymptotically stable.

4.2. Numerical Example

The numerical examples are worked out by using high level language Matlab R2013a on an Intel(R) core running on Windows 10 Professional Version.

Example 1. Let Take where . Thus, . To test the efficiency and accuracy of our scheme, we present the DMP inverse of as In Table 2, we give the errors , . The results show that the proposed method (4) converges to and has high-order accuracy.

Table 2: Numerical results of Scheme II (28) in Example 1 in Section 4.2.

5. Conclusions

We have developed two iterative methods for computing the DMP inverse. The proposed scheme has high-order accuracy and Scheme II can be implemented without constructing the projection . The stability, convergence analysis, and the error estimate of our schemes are given. Numerical examples show that our schemes have high-order accuracy and effectiveness. It is more interesting that we shall extend these methods to compute other generalized inverse, such as -generalized inverse [18, 20, 22].

Conflicts of Interest

No potential conflicts of interest were reported by the authors.

Acknowledgments

This work is supported partially by the National Natural Science Foundation of China (Grant no. 11361009), the Special Fund for Scientific and Technological Bases and Talents of Guangxi (Grant no. 2016AD05050), and the Special Fund for Bagui Scholars of Guangxi.

References

  1. H. Zhang and H. Yin, “Conjugate gradient least squares algorithm for solving the generalized coupled Sylvester matrix equations,” Computers & Mathematics with Applications. An International Journal, vol. 73, no. 12, pp. 2529–2547, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  2. I. Kyrchei, “Weighted singular value decomposition and determinantal representations of the quaternion weighted Moore-Penrose inverse,” Applied Mathematics and Computation, vol. 309, pp. 1–16, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. I. Kyrchei, “Explicit representation formulas for the minimum norm least squares solutions of some quaternion matrix equations,” Linear Algebra and Its Applications, vol. 438, no. 1, pp. 136–152, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  4. H. Wang, “Core-EP decomposition and its applications,” Linear Algebra and Its Applications, vol. 508, pp. 289–300, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  5. H.-A. Diao, Y. Wei, and P. Xie, “Small sample statistical condition estimation for the total least squares problem,” Numerical Algorithms, vol. 75, no. 2, pp. 435–455, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  6. M. Z. Nashed, Generalized Inverses and Applications, Academic Press, New York, NY, USA, 1976.
  7. A. J. Getson and F. C. Hsuan, AT,S(2)-Inverses and Their Statistical Applications, Lecture Notes in Statistics, Springer, Berlin, Germany, 1988. View at Publisher · View at Google Scholar · View at MathSciNet
  8. A. Ben-Israel and T. N. E. Greville, Generalized Inverses: Theory and Applications, Springer, New York, NY, USA, 2nd edition, 2003. View at MathSciNet
  9. B. Zheng and R. B. Bapat, “Generalized inverse AT,S(2) and a rank equation,” Applied Mathematics and Computation, vol. 155, no. 2, pp. 407–415, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  10. M. Z. Nashed and X. Chen, “Convergence of Newton-like methods for singular operator equations using outer inverses,” Numerische Mathematik, vol. 66, no. 2, pp. 235–257, 1993. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. O. M. Baksalary and G. Trenkler, “Core inverse of matrices,” Linear and Multilinear Algebra, vol. 58, no. 5-6, pp. 681–697, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. S. B. Malik and N. Thome, “On a new generalized inverse for matrices of an arbitrary index,” Applied Mathematics and Computation, vol. 226, pp. 575–580, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. A. Herrero, F. J. Ramírez, and N. Thome, “Relationships between different sets involving group and Drazin projectors and nonnegativity,” Linear Algebra and Its Applications, vol. 438, no. 4, pp. 1688–1699, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  14. L. Lebtahi and N. Thome, “The spectral characterization of k-generalized projectors,” Linear Algebra and Its Applications, vol. 20, pp. 572–575, 2007. View at Google Scholar
  15. A. Yu and C. Deng, “Characterizations of DMP inverse in a Hilbert space,” Calcolo. A Quarterly on Numerical Analysis and Theory of Computation, vol. 53, no. 3, pp. 331–341, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  16. C. Deng and A. Yu, Relationships between DMP Relation and Some Partial Orders, Elsevier Science, 2015.
  17. W. Li and Z. Li, “A family of iterative methods for computing the approximate inverse of a square matrix and inner inverse of a non-square matrix,” Applied Mathematics and Computation, vol. 215, no. 9, pp. 3433–3442, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. X. Liu, C. Hu, and Y. Yu, “Further results on iterative methods for computing generalized inverses,” Journal of Computational and Applied Mathematics, vol. 234, no. 3, pp. 684–694, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. J. Zhong, X. Liu, G. Zhou, and Y. Yu, “A new iterative method for computing the Drazin inverse,” Filomat, vol. 26, no. 3, pp. 597–606, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  20. X. Liu, H. Jin, and Y. Yu, “Higher-order convergent iterative method for computing the generalized inverse and its application to Toeplitz matrices,” Linear Algebra and Its Applications, vol. 439, no. 6, pp. 1635–1650, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  21. F. Soleymani and P. S. Stanimirović, “A higher order iterative method for computing the Drazin inverse,” The Scientific World Journal, vol. 2013, Article ID 708647, 11 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Srivastava and D. K. Gupta, “The iterative methods for AT,S(2) of the bounded linear operator between Banach spaces,” Applied Mathematics and Computation, vol. 49, no. 1-2, pp. 383–396, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  23. H. Chen and Y. Wang, “A family of higher-order convergent iterative methods for computing the Moore-Penrose inverse,” Applied Mathematics and Computation, vol. 218, no. 8, pp. 4012–4016, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. F. Soleymani and P. S. Stanimirović, “A note on the stability of a th order iteration for finding generalized inverses,” Applied Mathematics Letters, vol. 28, no. 2, pp. 77–81, 2014. View at Google Scholar
  25. V. Y. Pan, F. Soleymani, and L. Zhao, “An efficient computation of generalized inverse of a matrix,” Applied Mathematics and Computation, vol. 316, pp. 89–101, 2018. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus