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JACOBI-TYPE ALGORITHM FOR LOW RANK ORTHOGONAL APPROXIMATION OF SYMMETRIC TENSORS AND ITS CONVERGENCE ANALYSIS

机译:雅各比型算法,用于对称张量的低级正交近似及其收敛分析

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摘要

In this paper, we propose a Jacobi-type algorithm to solve the low rank orthogonal approximation problem of symmetric tensors. This algorithm includes as a special case the well-known Jacobi CoM2 algorithm for the approximate orthogonal diagonalization problem of symmetric tensors. We study the global convergence of this algorithm under a gradient based ordering for a special case: the best rank-2 orthogonal approximation of 3rd order symmetric tensors, and prove that an accumulation point is the unique limit point under some conditions. We also propose a proximal variant of this algorithm in general case, and prove its global convergence without any further condition. Numerical experiments are presented to show the efficiency of this algorithm.
机译:在本文中,我们提出了一种Jacobi型算法来解决对称张量的低等级正交近似问题。 该算法包括一种特殊情况,是众所周知的Jacobi COM2算法,用于对称张量的近似正交对值问题。 我们研究了该算法在基于梯度的订购下的全局收敛性:最佳的秩-2级 - 2正交对称对称张量的正交近似,并证明积累点在某些条件下是唯一的极限点。 在通常的情况下,我们还提出了该算法的近端变体,并证明其全局收敛而没有任何进一步的条件。 提出了数值实验以显示该算法的效率。

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