An inexact augmented Lagrangian method for computing strongly orthogonal decompositions of tensors

Shenglong Hu ()
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Shenglong Hu: Hangzhou Dianzi University

Computational Optimization and Applications, 2020, vol. 75, issue 3, No 6, 737 pages

Abstract: Abstract A strongly orthogonal decomposition of a tensor is a rank one tensor decomposition with the two component vectors in each mode of any two rank one tensors are either colinear or orthogonal. A strongly orthogonal decomposition with few number of rank one tensors is favorable in applications, which can be represented by a matrix-tensor multiplication with orthogonal factor matrices and a sparse tensor; and such a decomposition with the minimum number of rank one tensors is a strongly orthogonal rank decomposition. Any tensor has a strongly orthogonal rank decomposition. In this article, computing a strongly orthogonal rank decomposition is equivalently reformulated as solving an optimization problem. Different from the ill-posedness of the usual optimization reformulation for the tensor rank decomposition problem, the optimization reformulation of the strongly orthogonal rank decomposition of a tensor is well-posed. Each feasible solution of the optimization problem gives a strongly orthogonal decomposition of the tensor; and a global optimizer gives a strongly orthogonal rank decomposition, which is however difficult to compute. An inexact augmented Lagrangian method is proposed to solve the optimization problem. The augmented Lagrangian subproblem is solved by a proximal alternating minimization method, with the advantage that each subproblem has a closed formula solution and the factor matrices are kept orthogonal during the iteration. Thus, the algorithm always can return a feasible solution and thus a strongly orthogonal decomposition for any given tensor. Global convergence of this algorithm to a critical point is established without any further assumption. Extensive numerical experiments are conducted, and show that the proposed algorithm is quite promising in both efficiency and accuracy.

Keywords: Strongly orthogonal decomposition of a tensor; Augmented Lagrangian method; Strongly orthogonal rank; 15A69; 90C26 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10589-019-00128-3

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