Unitary-Invariant Decomposition of Reducible Total Least Squares Core Problems
Abstract
The analysis of a total least squares problem can be reduced to that of an associated core problem, which typically has lower dimension and improved solubility properties. Nevertheless, even a core problem may remain reducible, admitting further decomposition into irreducible component sub- problems with simpler structure and better analytical properties. We seek the finest decomposition under unitary transformations over C; for real data this decomposition can be strictly finer than the finest one under real orthogonal transformations. We split the data matrix into row blocks associated with the singular subspaces of the system matrix and consider the graph whose ver- tices are the blocks and whose edges correspond to nonzero cross-products. After preliminary splittings that make every nonzero cross-product a scalar multiple of a unitary matrix, we show that the components can be obtained directly from these blocks if and only if the covariance matrices along all cycles of the graph are scalar matrices. Otherwise, there is a chordless cycle whose covariance matrix is not a scalar matrix, and splitting the blocks on this cy- cle along the eigenspaces of the covariance matrix removes the obstruction without changing the decomposition. Based on these results, we propose a recursive strategy that finds all irreducible components over the complex field. The strategy is exact and theoretical rather than an efficient numerical algo- rithm; a small floating-point computation illustrates it. The results depend only on the core problem and therefore apply to general linear approximation problems AX B.
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BibTeX
@misc{yu2026unitaryinvariant,
title = {{Unitary-Invariant Decomposition of Reducible Total Least Squares Core Problems}},
author = {Sijia Yu and Bruno Carpentieri and Yan-Fei Jing},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.08091},
eprint = {2605.08091},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.08091},
}
RIS
TY - UNPB
TI - Unitary-Invariant Decomposition of Reducible Total Least Squares Core Problems
AU - Sijia Yu
AU - Bruno Carpentieri
AU - Yan-Fei Jing
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2605.08091
UR - https://arxiv.org/abs/2605.08091
AB - The analysis of a total least squares problem can be reduced to that of an associated core problem, which typically has lower dimension and improved solubility properties. Nevertheless, even a core problem may remain reducible, admitting further decomposition into irreducible component sub- problems with simpler structure and better analytical properties. We seek the finest decomposition under unitary transformations over C; for real data this decomposition can be strictly finer than the finest one under real orthogonal transformations. We split the data matrix into row blocks associated with the singular subspaces of the system matrix and consider the graph whose ver- tices are the blocks and whose edges correspond to nonzero cross-products. After preliminary splittings that make every nonzero cross-product a scalar multiple of a unitary matrix, we show that the components can be obtained directly from these blocks if and only if the covariance matrices along all cycles of the graph are scalar matrices. Otherwise, there is a chordless cycle whose covariance matrix is not a scalar matrix, and splitting the blocks on this cy- cle along the eigenspaces of the covariance matrix removes the obstruction without changing the decomposition. Based on these results, we propose a recursive strategy that finds all irreducible components over the complex field. The strategy is exact and theoretical rather than an efficient numerical algo- rithm; a small floating-point computation illustrates it. The results depend only on the core problem and therefore apply to general linear approximation problems AX B.
ER -