On Polar Decomposition of Tensors with Einstein Product and a Novel Iterative Parametric Method

R. Erfanifar, Masoud Hajarian and Khosro Sayevand
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Abstract

. This study aims to investigate the polar decomposition of tensors with the Einstein product for the first time. The polar decomposition of tensors can be computed using the singular value decomposition of the tensors with the Einstein product. In the following, some iterative methods for finding the polar decomposition of matrices have been developed into iterative methods to compute the polar decomposition of tensors. Then, we propose a novel parametric iterative method to find the polar decomposition of tensors. Under the obtained conditions, we prove that the proposed parametric method has the order of convergence four. In every iteration of the proposed method, only four Einstein products are required, while other iterative methods need to calculate multiple Einstein products and one tensor inversion in each iteration. Thus, the new method is superior in terms of efficiency index. Finally, the numerical comparisons performed among several well-known methods, show that the proposed method is remarkably efficient and accurate.
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论带有爱因斯坦积的张量极性分解和一种新颖的迭代参数法
.本研究旨在首次研究具有爱因斯坦积的张量的极性分解。张量的极性分解可以利用张量的爱因斯坦积奇异值分解来计算。下面,我们将一些求矩阵极性分解的迭代方法发展为计算张量极性分解的迭代方法。然后,我们提出了一种新颖的参数迭代法来求张量的极性分解。在所获得的条件下,我们证明了所提出的参数方法具有四阶收敛性。在拟议方法的每次迭代中,只需要四个爱因斯坦积,而其他迭代方法每次迭代需要计算多个爱因斯坦积和一次张量反演。因此,新方法在效率指数方面更胜一筹。最后,对几种著名方法进行的数值比较表明,所提出的方法具有显著的高效性和准确性。
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