A new iterative method to find the Moore–Penrose inverse

In this paper, we introduce a new efficient iterative method to find the Moore–Penrose inverse of a singular or rectangular real (or complex) matrix, which has five matrix-matrix multiplications per iteration. It is proved that the proposed method is fourth-order convergent to the Moore–Penrose inverse. Numerical results are obtained using random matrices of sizes n×n and n×(n+10), n=100,200,300,400. The average number of iterations, the average total number of matrix-matrix multiplications, the average precision, and the average elapsed execution time of two methods are computed using ten matrices in each dimension. Based on this comparison, our new method can be considered as an efficient fast method with low computational cost.

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Publication Details

Journal
Applicationes Mathematicae
Published
2025-09-24
DOI
https://doi.org/10.4064/am2524-6-2025
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

A new iterative method to find the Moore–Penrose inverse

Salman Sheikhi, Hamid Esmaeili
Applicationes Mathematicae
Matrix Theory and Algorithms
article

A new iterative method to find the Moore–Penrose inverse

Salman Sheikhi, Hamid Esmaeili
article en

Abstract

In this paper, we introduce a new efficient iterative method to find the Moore–Penrose inverse of a singular or rectangular real (or complex) matrix, which has five matrix-matrix multiplications per iteration. It is proved that the proposed method is fourth-order convergent to the Moore–Penrose inverse. Numerical results are obtained using random matrices of sizes n×n and n×(n+10), n=100,200,300,400. The average number of iterations, the average total number of matrix-matrix multiplications, the average precision, and the average elapsed execution time of two methods are computed using ten matrices in each dimension. Based on this comparison, our new method can be considered as an efficient fast method with low computational cost.

Applicationes Mathematicae
Bu-Ali Sina University (IR)
Openalex Percentile: Top 73%
Matrix Theory and Algorithms
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