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Title: Properties and methods for finding the best rank-one approximation to higher-order tensors
Authors: Yang, Y
Yang, Q
Qi, L 
Keywords: Algorithm
Best rank-one approximation
Nonnegative tensors
Nuclear norm regularization
Strong duality
Issue Date: 2014
Publisher: Springer
Source: Computational optimization and applications, 2014, v. 58, no. 1, p. 105-132 How to cite?
Journal: Computational optimization and applications 
Abstract: The problem of finding the best rank-one approximation to higher-order tensors has extensive engineering and statistical applications. It is well-known that this problem is equivalent to a homogeneous polynomial optimization problem. In this paper, we study theoretical results and numerical methods of this problem, particularly focusing on the 4-th order symmetric tensor case. First, we reformulate the polynomial optimization problem to a matrix programming, and show the equivalence between these two problems. Then, we prove that there is no duality gap between the reformulation and its Lagrangian dual problem. Concerning the approaches to deal with the problem, we propose two relaxed models. The first one is a convex quadratic matrix optimization problem regularized by the nuclear norm, while the second one is a quadratic matrix programming regularized by a truncated nuclear norm, which is a D.C. function and therefore is nonconvex. To overcome the difficulty of solving this nonconvex problem, we approximate the nonconvex penalty by a convex term. We propose to use the proximal augmented Lagrangian method to solve these two relaxed models. In order to obtain a global solution, we propose an alternating least eigenvalue method after solving the relaxed models and prove its convergence. Numerical results presented in the last demonstrate, especially for nonpositive tensors, the effectiveness and efficiency of our proposed methods.
ISSN: 0926-6003
EISSN: 1573-2894
DOI: 10.1007/s10589-013-9617-9
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