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A sparse optimization approach for simultaneous orthogonal tensor diagonalization
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DOI：10.1016/j.amc.2024.129203.png)
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Abstract
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This paper presents a sparse optimization method for the simultaneous orthogonal tensor diagonalization. The model treats off-diagonal elements of tensors as entities requiring sparsity, guided by an l(1) norm regularizer to optimize the diagonalization process. A gradient-based alternating multi-block Jacobi-AMB algorithm is developed to address the optimization problem on the product of orthogonal groups. We establish the global convergence based on the Kurdyka-Lojasiewicz property. Numerical experiments demonstrate that the Jacobi-AMB performs well in efficiency; under certain circumstances, its stability and effectiveness also perform well.
Keywords:
Sparse optimization
Simultaneous diagonalization
Jacobi-type algorithm
Global convergence
Kurdyka-Lojasiewicz property
Journal
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3.4
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2.3W
Citations:
3.3W